{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/Tolga/Dropbox/Ukraine_SurveyExp/Repository/code/REPLICATION/Replication_PSRM/RedLines_Replication.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}20 Nov 2025, 16:26:56
{txt}
{com}. 
. use "RedLines_dataset.dta"
{txt}
{com}. 
. ********************************************************************************
. *Generating additional variables for analysis
. ********************************************************************************
. 
. gen baseline_support=q30 if wave==1 | wave==2&online==1
{txt}(3,742 missing values generated)

{com}.         replace baseline_support=q1_experiment if wave==2&telephone==1
{txt}(2,013 real changes made)

{com}.         
.         replace baseline_support=. if baseline_support>10
{txt}(501 real changes made, 501 to missing)

{com}.         replace baseline_support=baseline_support/10
{txt}(4,685 real changes made)

{com}.         
. gen poison_pill=.
{txt}(8,487 missing values generated)

{com}.         replace poison_pill=1 if q30_1!=. & wave==1 | q30_1!=. & wave==2&online==1
{txt}(1,207 real changes made)

{com}.         replace poison_pill=2 if q30_2!=. & wave==1 | q30_2!=. & wave==2&online==1
{txt}(1,184 real changes made)

{com}.         replace poison_pill=3 if q30_3!=. & wave==1 | q30_3!=. & wave==2&online==1
{txt}(1,177 real changes made)

{com}.         replace poison_pill=4 if q30_4!=. & wave==1 | q30_4!=. & wave==2&online==1
{txt}(1,177 real changes made)

{com}.         
.         replace poison_pill=1 if sample_q2==1 & wave==2&telephone==1
{txt}(527 real changes made)

{com}.         replace poison_pill=2 if sample_q2==2 & wave==2&telephone==1
{txt}(521 real changes made)

{com}.         replace poison_pill=3 if sample_q2==3 & wave==2&telephone==1
{txt}(504 real changes made)

{com}.         replace poison_pill=4 if sample_q2==4 & wave==2&telephone==1
{txt}(461 real changes made)

{com}.         
.         label define poison_pill_lab 1 "Zelensky resigns" 2 "Crimea recognized as Russia" 3 "Let LNR/DNR vote" 4 "Never join EU"
{txt}
{com}. label values poison_pill poison_pill_lab
{txt}
{com}.         
. gen postpoison_support=q30_1 if poison_pill==1 & wave==1 | poison_pill==1 & wave==2 &online==1
{txt}(7,280 missing values generated)

{com}.         replace postpoison_support=q30_2 if poison_pill==2 & wave==1 | poison_pill==2 & wave==2 &online==1
{txt}(1,184 real changes made)

{com}.         replace postpoison_support=q30_3 if poison_pill==3 & wave==1 | poison_pill==3 & wave==2 &online==1
{txt}(1,177 real changes made)

{com}.         replace postpoison_support=q30_4 if poison_pill==4      & wave==1 | poison_pill==4 & wave==2 &online==1
{txt}(1,177 real changes made)

{com}.         replace postpoison_support=q2 if wave==2&telephone==1
{txt}(2,013 real changes made)

{com}.         
.         replace postpoison_support=. if postpoison_support>10
{txt}(468 real changes made, 468 to missing)

{com}.         replace postpoison_support=postpoison_support/10
{txt}(2,493 real changes made)

{com}.         
. gen delta_support=postpoison_support-baseline_support
{txt}(2,493 missing values generated)

{com}. 
. bys wave online: sum delta_support

{txt}{hline}
-> wave = 1, online = 0

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
delta_supp~t {c |}{res}      2,733   -.3505671    .4370604         -1          1

{txt}{hline}
-> wave = 1, online = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
delta_supp~t {c |}{res}      1,481   -.3325456    .3693962         -1          1

{txt}{hline}
-> wave = 2, online = 0

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
delta_supp~t {c |}{res}      1,780   -.2854494    .4348332         -1          1

{txt}{hline}
-> wave = 2, online = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
delta_supp~t {c |}{res}          0

{txt}
{com}. bys wave online: sum baseline_support

{txt}{hline}
-> wave = 1, online = 0

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
baseline_s~t {c |}{res}      2,856    .6028011     .412891          0          1

{txt}{hline}
-> wave = 1, online = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
baseline_s~t {c |}{res}      1,547    .6261797    .3657823          0          1

{txt}{hline}
-> wave = 2, online = 0

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
baseline_s~t {c |}{res}      1,854    .5031823    .4239149          0          1

{txt}{hline}
-> wave = 2, online = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
baseline_s~t {c |}{res}          0

{txt}
{com}. 
. 
. sum pca_violexposure, de

                   {txt}Scores for component 1
{hline 61}
      Percentiles      Smallest
 1%    {res}-1.972956      -1.972956
{txt} 5%    {res}-1.972956      -1.972956
{txt}10%    {res}-1.440323      -1.972956       {txt}Obs         {res}      5,483
{txt}25%    {res}-1.174006      -1.972956       {txt}Sum of wgt. {res}      5,483

{txt}50%    {res}-.3744698                      {txt}Mean          {res} 2.33e-10
                        {txt}Largest       Std. dev.     {res} 1.536362
{txt}75%    {res} .7996396       5.235899
{txt}90%    {res} 2.194698       5.235899       {txt}Variance      {res} 2.360408
{txt}95%    {res} 3.180504       5.235899       {txt}Skewness      {res} 1.166171
{txt}99%    {res} 4.888342       5.235899       {txt}Kurtosis      {res} 4.096425
{txt}
{com}. gen above_med_expos=0
{txt}
{com}.         replace above_med_expos=1 if pca_violexposure>=-.37 & wave==1 |  pca_violexposure>=-.37 & wave==2 & online==1
{txt}(3,682 real changes made)

{com}.         replace above_med_expos=. if pca_violexposure==. & wave==1 |  pca_violexposure==. & wave==2 & online==1
{txt}(991 real changes made, 991 to missing)

{com}. 
. sum gunfire_exposure_norm if wave==2&telephone==1, de

                    {txt}gunfire_exposure_norm
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,961
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,961

{txt}50%    {res}        0                      {txt}Mean          {res} .2738399
                        {txt}Largest       Std. dev.     {res} .3793308
{txt}75%    {res}       .6              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1438918
{txt}95%    {res}        1              1       {txt}Skewness      {res} .9948123
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.387399
{txt}
{com}. *Median=0
.         replace above_med_expos=1 if gunfire_exposure_norm>0 & wave==2&telephone==1
{txt}(861 real changes made)

{com}.         replace above_med_expos=0 if gunfire_exposure_norm<=0 & wave==2&telephone==1
{txt}(0 real changes made)

{com}.         replace above_med_expos=. if gunfire_exposure_norm==. & wave==2&telephone==1
{txt}(52 real changes made, 52 to missing)

{com}. 
.         tab above_med_expos,m

{txt}above_med_e {c |}
       xpos {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      3,944       46.47       46.47
{txt}          1 {c |}{res}      3,500       41.24       87.71
{txt}          . {c |}{res}      1,043       12.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00
{txt}
{com}. 
. 
. *Does military optimism drive lower baseline support for agreement in the 
. *poison pill experiment? Comparing online wave 1 and telephone wave 1 using
. *military optimism on RHS
. 
. *Rescale 
. 
. 
. foreach v in milsuccess_q10 milsuccess_q11 milsuccess_q12 milsuccess_q13{c -(}
{txt}  2{com}.         numlabel `v', add
{txt}  3{com}.         tab `v'
{txt}  4{com}. {c )-}
{res}
 {txt}Army Success - 10. {c |}
         The use of {c |}
Bayraktar and other {c |}
      drones by the {c |}
   Ukrainian milita {c |}      Freq.     Percent        Cum.
{hline 20}{c +}{hline 35}
0. no effect at all {c |}{res}         41        0.63        0.63
{txt}               1. 1 {c |}{res}         12        0.19        0.82
{txt}               2. 2 {c |}{res}         16        0.25        1.07
{txt}               3. 3 {c |}{res}         33        0.51        1.58
{txt}               4. 4 {c |}{res}         39        0.60        2.18
{txt}     5. some effect {c |}{res}        773       11.94       14.12
{txt}               6. 6 {c |}{res}        204        3.15       17.27
{txt}               7. 7 {c |}{res}        426        6.58       23.85
{txt}               8. 8 {c |}{res}        686       10.60       34.45
{txt}               9. 9 {c |}{res}        413        6.38       40.82
{txt}10. very big effect {c |}{res}      3,415       52.75       93.57
{txt}    11. Hard to say {c |}{res}        371        5.73       99.30
{txt}         12. Refuse {c |}{res}         45        0.70      100.00
{txt}{hline 20}{c +}{hline 35}
              Total {c |}{res}      6,474      100.00

 {txt}Army Success - 11. {c |}
 The quality of the {c |}
 military equipment {c |}
   and weapons that {c |}
             Ukrain {c |}      Freq.     Percent        Cum.
{hline 20}{c +}{hline 35}
0. no effect at all {c |}{res}         55        0.85        0.85
{txt}               1. 1 {c |}{res}          8        0.12        0.97
{txt}               2. 2 {c |}{res}         29        0.45        1.42
{txt}               3. 3 {c |}{res}         48        0.74        2.16
{txt}               4. 4 {c |}{res}         56        0.86        3.03
{txt}     5. some effect {c |}{res}      1,097       16.94       19.97
{txt}               6. 6 {c |}{res}        251        3.88       23.85
{txt}               7. 7 {c |}{res}        526        8.12       31.97
{txt}               8. 8 {c |}{res}        715       11.04       43.02
{txt}               9. 9 {c |}{res}        445        6.87       49.89
{txt}10. very big effect {c |}{res}      2,769       42.77       92.66
{txt}    11. Hard to say {c |}{res}        428        6.61       99.27
{txt}         12. Refuse {c |}{res}         47        0.73      100.00
{txt}{hline 20}{c +}{hline 35}
              Total {c |}{res}      6,474      100.00

 {txt}Army Success - 12. {c |}
   The strength and {c |}
  solidarity of the {c |}
   Ukrainian people {c |}      Freq.     Percent        Cum.
{hline 20}{c +}{hline 35}
0. no effect at all {c |}{res}         49        0.76        0.76
{txt}               1. 1 {c |}{res}          7        0.11        0.86
{txt}               2. 2 {c |}{res}          9        0.14        1.00
{txt}               3. 3 {c |}{res}         27        0.42        1.42
{txt}               4. 4 {c |}{res}         37        0.57        1.99
{txt}     5. some effect {c |}{res}        538        8.31       10.30
{txt}               6. 6 {c |}{res}         98        1.51       11.82
{txt}               7. 7 {c |}{res}        278        4.29       16.11
{txt}               8. 8 {c |}{res}        551        8.51       24.62
{txt}               9. 9 {c |}{res}        421        6.50       31.12
{txt}10. very big effect {c |}{res}      4,232       65.37       96.49
{txt}    11. Hard to say {c |}{res}        188        2.90       99.40
{txt}         12. Refuse {c |}{res}         39        0.60      100.00
{txt}{hline 20}{c +}{hline 35}
              Total {c |}{res}      6,474      100.00

 {txt}Army Success - 13. {c |}
 The low quality of {c |}
   the Russian army {c |}      Freq.     Percent        Cum.
{hline 20}{c +}{hline 35}
0. no effect at all {c |}{res}        439        6.78        6.78
{txt}               1. 1 {c |}{res}         48        0.74        7.52
{txt}               2. 2 {c |}{res}        125        1.93        9.45
{txt}               3. 3 {c |}{res}        190        2.93       12.39
{txt}               4. 4 {c |}{res}        219        3.38       15.77
{txt}     5. some effect {c |}{res}      2,319       35.82       51.59
{txt}               6. 6 {c |}{res}        265        4.09       55.68
{txt}               7. 7 {c |}{res}        396        6.12       61.80
{txt}               8. 8 {c |}{res}        424        6.55       68.35
{txt}               9. 9 {c |}{res}        156        2.41       70.76
{txt}10. very big effect {c |}{res}      1,152       17.79       88.55
{txt}    11. Hard to say {c |}{res}        673       10.40       98.95
{txt}         12. Refuse {c |}{res}         68        1.05      100.00
{txt}{hline 20}{c +}{hline 35}
              Total {c |}{res}      6,474      100.00
{txt}
{com}. 
. gen milsuccess_q9_norm=milsuccess_q9 if milsuccess_q9<=10
{txt}(2,268 missing values generated)

{com}.         replace milsuccess_q9_norm=(milsuccess_q9_norm)/10
{txt}(6,172 real changes made)

{com}. 
. gen milsuccess_q10_norm=milsuccess_q10 if milsuccess_q10<=10
{txt}(2,429 missing values generated)

{com}.         replace milsuccess_q10_norm=(milsuccess_q10_norm)/10
{txt}(6,017 real changes made)

{com}. 
. gen milsuccess_q11_norm=milsuccess_q11 if milsuccess_q11<=10
{txt}(2,488 missing values generated)

{com}. replace milsuccess_q11_norm=(milsuccess_q11_norm)/10
{txt}(5,944 real changes made)

{com}. 
. gen milsuccess_q12_norm=milsuccess_q12 if milsuccess_q12<=10
{txt}(2,240 missing values generated)

{com}. replace milsuccess_q12_norm=(milsuccess_q12_norm)/10
{txt}(6,198 real changes made)

{com}. 
. gen milsuccess_q13_norm=milsuccess_q13 if milsuccess_q13<=10
{txt}(2,754 missing values generated)

{com}. replace milsuccess_q13_norm=(milsuccess_q13_norm)/10
{txt}(5,294 real changes made)

{com}.         
. 
. ********************************************************************************
. *Replicating main text figures
. ********************************************************************************
. 
. 
. ***FIGURE 4: Experimental primes influence public support for settlements
. 
. 
. matrix survey_matrix = J(15,4,.)
{txt}
{com}.         
. summarize baseline_support if wave==1 & online==1, detail

                      {txt}baseline_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,547
{txt}25%    {res}       .3              0       {txt}Sum of wgt. {res}      1,547

{txt}50%    {res}       .7                      {txt}Mean          {res} .6261797
                        {txt}Largest       Std. dev.     {res} .3657823
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1337967
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.5447105
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.881899
{txt}
{com}. 
. matrix survey_matrix[1,1] = 1
{txt}
{com}. matrix survey_matrix[1,2] = r(mean)
{txt}
{com}. matrix survey_matrix[1,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[1,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize postpoison_support if poison_pill==3 & wave==1 & online==1, detail

                     {txt}postpoison_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        394
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        394

{txt}50%    {res}       .4                      {txt}Mean          {res}  .393401
                        {txt}Largest       Std. dev.     {res} .3784354
{txt}75%    {res}       .7              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1432133
{txt}95%    {res}        1              1       {txt}Skewness      {res} .2906278
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.573419
{txt}
{com}. 
. matrix survey_matrix[2,1] = 1.25
{txt}
{com}. matrix survey_matrix[2,2] = r(mean)
{txt}
{com}. matrix survey_matrix[2,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[2,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize postpoison_support if poison_pill==1 &wave==1 & online==1, detail

                     {txt}postpoison_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        367
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        367

{txt}50%    {res}       .1                      {txt}Mean          {res} .2940054
                        {txt}Largest       Std. dev.     {res} .3632985
{txt}75%    {res}       .5              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1319858
{txt}95%    {res}        1              1       {txt}Skewness      {res} .8141572
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.147338
{txt}
{com}. 
. matrix survey_matrix[3,1] = 1.5
{txt}
{com}. matrix survey_matrix[3,2] = r(mean)
{txt}
{com}. matrix survey_matrix[3,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[3,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize postpoison_support if poison_pill==2&wave==1 & online==1, detail

                     {txt}postpoison_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        389
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        389

{txt}50%    {res}        0                      {txt}Mean          {res}  .196401
                        {txt}Largest       Std. dev.     {res} .2879673
{txt}75%    {res}       .4              1
{txt}90%    {res}       .7              1       {txt}Variance      {res} .0829252
{txt}95%    {res}       .8              1       {txt}Skewness      {res} 1.263469
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.411326
{txt}
{com}. 
. matrix survey_matrix[4,1] = 1.75
{txt}
{com}. matrix survey_matrix[4,2] = r(mean)
{txt}
{com}. matrix survey_matrix[4,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[4,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize postpoison_support if poison_pill==4&wave==1 & online==1, detail

                     {txt}postpoison_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        415
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        415

{txt}50%    {res}        0                      {txt}Mean          {res} .2274699
                        {txt}Largest       Std. dev.     {res} .3217811
{txt}75%    {res}       .5              1
{txt}90%    {res}       .8              1       {txt}Variance      {res} .1035431
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.166192
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.039582
{txt}
{com}. 
. matrix survey_matrix[5,1] = 2
{txt}
{com}. matrix survey_matrix[5,2] = r(mean)
{txt}
{com}. matrix survey_matrix[5,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[5,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. 
. summarize baseline_support if wave==1 & online==0, detail

                      {txt}baseline_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,856
{txt}25%    {res}       .1              0       {txt}Sum of wgt. {res}      2,856

{txt}50%    {res}       .7                      {txt}Mean          {res} .6028011
                        {txt}Largest       Std. dev.     {res}  .412891
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res}  .170479
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.4321992
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.546907
{txt}
{com}. 
. matrix survey_matrix[6,1] = 2.5
{txt}
{com}. matrix survey_matrix[6,2] = r(mean)
{txt}
{com}. matrix survey_matrix[6,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[6,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize postpoison_support if poison_pill==3&wave==1 & online==0, detail

                     {txt}postpoison_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        692
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        692

{txt}50%    {res}       .2                      {txt}Mean          {res} .3754335
                        {txt}Largest       Std. dev.     {res} .4153745
{txt}75%    {res}       .8              1
{txt}90%    {res}        1              1       {txt}Variance      {res}  .172536
{txt}95%    {res}        1              1       {txt}Skewness      {res} .4730795
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.542331
{txt}
{com}. 
. matrix survey_matrix[7,1] = 2.75
{txt}
{com}. matrix survey_matrix[7,2] = r(mean)
{txt}
{com}. matrix survey_matrix[7,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[7,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize postpoison_support if poison_pill==1&wave==1 & online==0, detail

                     {txt}postpoison_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        754
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        754

{txt}50%    {res}        0                      {txt}Mean          {res} .2197613
                        {txt}Largest       Std. dev.     {res} .3321537
{txt}75%    {res}       .5              1
{txt}90%    {res}       .8              1       {txt}Variance      {res} .1103261
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.258294
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.241977
{txt}
{com}. 
. matrix survey_matrix[8,1] = 3
{txt}
{com}. matrix survey_matrix[8,2] = r(mean)
{txt}
{com}. matrix survey_matrix[8,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[8,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize postpoison_support if poison_pill==2&wave==1 & online==0, detail

                     {txt}postpoison_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        721
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        721

{txt}50%    {res}        0                      {txt}Mean          {res} .1762829
                        {txt}Largest       Std. dev.     {res} .3030961
{txt}75%    {res}       .3              1
{txt}90%    {res}       .6              1       {txt}Variance      {res} .0918673
{txt}95%    {res}        1              1       {txt}Skewness      {res}  1.56856
{txt}99%    {res}        1              1       {txt}Kurtosis      {res}  4.22342
{txt}
{com}. 
. matrix survey_matrix[9,1] = 3.25
{txt}
{com}. matrix survey_matrix[9,2] = r(mean)
{txt}
{com}. matrix survey_matrix[9,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[9,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize postpoison_support if poison_pill==4&wave==1 & online==0, detail

                     {txt}postpoison_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        681
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        681

{txt}50%    {res}        0                      {txt}Mean          {res} .1951542
                        {txt}Largest       Std. dev.     {res}  .327679
{txt}75%    {res}       .5              1
{txt}90%    {res}       .8              1       {txt}Variance      {res} .1073735
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.464996
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.759878
{txt}
{com}. 
. matrix survey_matrix[10,1] = 3.5
{txt}
{com}. matrix survey_matrix[10,2] = r(mean)
{txt}
{com}. matrix survey_matrix[10,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[10,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. 
. summarize baseline_support if wave==2 & online==0, detail

                      {txt}baseline_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,854
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,854

{txt}50%    {res}       .5                      {txt}Mean          {res} .5031823
                        {txt}Largest       Std. dev.     {res} .4239149
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1797038
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.0376278
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.329777
{txt}
{com}. 
. matrix survey_matrix[11,1] = 4
{txt}
{com}. matrix survey_matrix[11,2] = r(mean)
{txt}
{com}. matrix survey_matrix[11,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[11,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize postpoison_support if poison_pill==3&wave==2 & online==0, detail

                     {txt}postpoison_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        478
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        478

{txt}50%    {res}        0                      {txt}Mean          {res} .2725941
                        {txt}Largest       Std. dev.     {res} .3655242
{txt}75%    {res}       .5              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1336079
{txt}95%    {res}        1              1       {txt}Skewness      {res} .9382837
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.380918
{txt}
{com}. 
. matrix survey_matrix[12,1] = 4.25
{txt}
{com}. matrix survey_matrix[12,2] = r(mean)
{txt}
{com}. matrix survey_matrix[12,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[12,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize postpoison_support if poison_pill==1&wave==2 & online==0, detail

                     {txt}postpoison_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        476
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        476

{txt}50%    {res}        0                      {txt}Mean          {res} .2581933
                        {txt}Largest       Std. dev.     {res} .3515321
{txt}75%    {res}       .5              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1235748
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.008497
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.606412
{txt}
{com}. 
. matrix survey_matrix[13,1] = 4.5
{txt}
{com}. matrix survey_matrix[13,2] = r(mean)
{txt}
{com}. matrix survey_matrix[13,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[13,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize postpoison_support if poison_pill==2&wave==2 & online==0, detail

                     {txt}postpoison_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        498
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        498

{txt}50%    {res}        0                      {txt}Mean          {res}  .135743
                        {txt}Largest       Std. dev.     {res} .2785554
{txt}75%    {res}        0              1
{txt}90%    {res}       .5              1       {txt}Variance      {res} .0775931
{txt}95%    {res}        1              1       {txt}Skewness      {res} 2.021003
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 5.931803
{txt}
{com}. 
. matrix survey_matrix[14,1] = 4.75
{txt}
{com}. matrix survey_matrix[14,2] = r(mean)
{txt}
{com}. matrix survey_matrix[14,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[14,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize postpoison_support if poison_pill==4&wave==2 & online==0, detail

                     {txt}postpoison_support
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        425
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        425

{txt}50%    {res}        0                      {txt}Mean          {res} .1778824
                        {txt}Largest       Std. dev.     {res} .3093362
{txt}75%    {res}       .4              1
{txt}90%    {res}       .6              1       {txt}Variance      {res} .0956889
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.556416
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 4.158753
{txt}
{com}. 
. matrix survey_matrix[15,1] = 5
{txt}
{com}. matrix survey_matrix[15,2] = r(mean)
{txt}
{com}. matrix survey_matrix[15,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[15,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. 
. svmat survey_matrix, names(sm_)
{txt}
{com}. 
. tw (pccapsym sm_3 sm_1 sm_4 sm_1, msymbol(point)) (scatter sm_2 sm_1 if sm_1==1 | sm_1==2.5 | sm_1==4, msymbol(O)) (scatter sm_2 sm_1 if sm_1==1.25 | sm_1==2.75 | sm_1==4.25, msymbol(dh)) (scatter sm_2 sm_1 if sm_1==1.5 | sm_1==3 | sm_1==4.5, msymbol(th)) (scatter sm_2 sm_1 if sm_1==1.75 | sm_1==3.25 | sm_1==4.75, msymbol(sh)) (scatter sm_2 sm_1 if sm_1==2 | sm_1==3.5 | sm_1==5, msymbol(oh)) ,  ///
>         xtitle("Survey waves") ytitle("Public support for settlement") xlabel(1.5 "Wave 1 online (July 1-7, 2022)" 3 "Wave 1 phone (July 1-12, 2022)" 4.5 "Wave 3 phone (May 26 - June 5, 2023)", angle(45) labsize(small)) legend(order( 2 "Baseline" 3 "Let LNR/DNR vote" 4 "Zelensky resigns" 5 "Crimea recognized as Russia" 6 "Never join EU") position(2) bmargin(medium))
{res}{txt}
{com}. 
. quietly graph export figure4.jpg, replace
{txt}
{com}. 
. drop sm_* 
{txt}
{com}. 
.         
. ***FIGURE 5: Percentage change in support for an agreement relative to the baseline
. 
. gen difference_in_support = postpoison_support - baseline_support
{txt}(2,493 missing values generated)

{com}. sum difference_in_support       

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
difference~t {c |}{res}      5,994   -.3267768    .4215203         -1          1
{txt}
{com}. 
. gen pct_change = (difference_in_support/baseline_support)*100
{txt}(4,028 missing values generated)

{com}. sum pct_change

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}pct_change {c |}{res}      4,459   -57.05062    58.18565       -100   899.9999
{txt}
{com}. 
. bys wave online poison_pill: sum difference_in_support

{txt}{hline}
-> wave = 1, online = 0, poison_pill = Zelensky resigns

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
difference~t {c |}{res}        729   -.3673525    .4450012         -1          1

{txt}{hline}
-> wave = 1, online = 0, poison_pill = Crimea recognized as Russia

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
difference~t {c |}{res}        692       -.425    .4174166         -1         .6

{txt}{hline}
-> wave = 1, online = 0, poison_pill = Let LNR/DNR vote

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
difference~t {c |}{res}        663   -.2334842    .4368879         -1          1

{txt}{hline}
-> wave = 1, online = 0, poison_pill = Never join EU

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
difference~t {c |}{res}        649   -.3719569    .4258789         -1          1

{txt}{hline}
-> wave = 1, online = 1, poison_pill = Zelensky resigns

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
difference~t {c |}{res}        347   -.3475504    .3800115         -1         .8

{txt}{hline}
-> wave = 1, online = 1, poison_pill = Crimea recognized as Russia

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
difference~t {c |}{res}        367   -.3885559    .3590057         -1          1

{txt}{hline}
-> wave = 1, online = 1, poison_pill = Let LNR/DNR vote

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
difference~t {c |}{res}        375   -.2274667     .349205         -1          1

{txt}{hline}
-> wave = 1, online = 1, poison_pill = Never join EU

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
difference~t {c |}{res}        392   -.3673469    .3694263         -1          1

{txt}{hline}
-> wave = 2, online = 0, poison_pill = Zelensky resigns

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
difference~t {c |}{res}        455   -.2301099    .4322299         -1          1

{txt}{hline}
-> wave = 2, online = 0, poison_pill = Crimea recognized as Russia

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
difference~t {c |}{res}        461   -.3661605    .4427977         -1          1

{txt}{hline}
-> wave = 2, online = 0, poison_pill = Let LNR/DNR vote

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
difference~t {c |}{res}        459     -.23878    .4331506         -1          1

{txt}{hline}
-> wave = 2, online = 0, poison_pill = Never join EU

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
difference~t {c |}{res}        405    -.308642    .4157177         -1          1

{txt}{hline}
-> wave = 2, online = 1, poison_pill = .

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
difference~t {c |}{res}          0

{txt}
{com}. 
. 
. matrix survey_matrix = J(12,4,.)
{txt}
{com}.         
. summarize pct_change if poison_pill==3 & wave==1 & online==1, detail

                         {txt}pct_change
{hline 61}
      Percentiles      Smallest
 1%    {res}     -100           -100
{txt} 5%    {res}     -100           -100
{txt}10%    {res}     -100           -100       {txt}Obs         {res}        321
{txt}25%    {res}     -100           -100       {txt}Sum of wgt. {res}        321

{txt}50%    {res}      -30                      {txt}Mean          {res}-35.96796
                        {txt}Largest       Std. dev.     {res} 64.06916
{txt}75%    {res}        0            150
{txt}90%    {res}        0       166.6667       {txt}Variance      {res} 4104.857
{txt}95%    {res} 33.33333            200       {txt}Skewness      {res} 3.436788
{txt}99%    {res}      150       599.9999       {txt}Kurtosis      {res} 32.50576
{txt}
{com}. matrix survey_matrix[1,1] = 1
{txt}
{com}. matrix survey_matrix[1,2] = r(mean)
{txt}
{com}. matrix survey_matrix[1,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[1,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize pct_change if poison_pill==1 &wave==1 & online==1, detail

                         {txt}pct_change
{hline 61}
      Percentiles      Smallest
 1%    {res}     -100           -100
{txt} 5%    {res}     -100           -100
{txt}10%    {res}     -100           -100       {txt}Obs         {res}        292
{txt}25%    {res}     -100           -100       {txt}Sum of wgt. {res}        292

{txt}50%    {res}-63.33333                      {txt}Mean          {res}-55.34043
                        {txt}Largest       Std. dev.     {res} 52.17811
{txt}75%    {res}-14.58334            100
{txt}90%    {res}        0            100       {txt}Variance      {res} 2722.555
{txt}95%    {res}        0            100       {txt}Skewness      {res} 2.677559
{txt}99%    {res}      100            400       {txt}Kurtosis      {res} 21.61134
{txt}
{com}. matrix survey_matrix[2,1] = 1.25
{txt}
{com}. matrix survey_matrix[2,2] = r(mean)
{txt}
{com}. matrix survey_matrix[2,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[2,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize pct_change if poison_pill==2&wave==1 & online==1, detail

                         {txt}pct_change
{hline 61}
      Percentiles      Smallest
 1%    {res}     -100           -100
{txt} 5%    {res}     -100           -100
{txt}10%    {res}     -100           -100       {txt}Obs         {res}        302
{txt}25%    {res}     -100           -100       {txt}Sum of wgt. {res}        302

{txt}50%    {res}      -90                      {txt}Mean          {res}-67.21539
                        {txt}Largest       Std. dev.     {res} 40.13893
{txt}75%    {res}    -37.5       33.33333
{txt}90%    {res}        0             50       {txt}Variance      {res} 1611.134
{txt}95%    {res}        0             50       {txt}Skewness      {res} .9872375
{txt}99%    {res} 33.33333            100       {txt}Kurtosis      {res} 3.188224
{txt}
{com}. matrix survey_matrix[3,1] = 1.5
{txt}
{com}. matrix survey_matrix[3,2] = r(mean)
{txt}
{com}. matrix survey_matrix[3,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[3,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize pct_change if poison_pill==4&wave==1 & online==1, detail

                         {txt}pct_change
{hline 61}
      Percentiles      Smallest
 1%    {res}     -100           -100
{txt} 5%    {res}     -100           -100
{txt}10%    {res}     -100           -100       {txt}Obs         {res}        339
{txt}25%    {res}     -100           -100       {txt}Sum of wgt. {res}        339

{txt}50%    {res}      -90                      {txt}Mean          {res}-63.42335
                        {txt}Largest       Std. dev.     {res} 46.19203
{txt}75%    {res}      -25       74.99999
{txt}90%    {res}        0       74.99999       {txt}Variance      {res} 2133.704
{txt}95%    {res}        0       166.6667       {txt}Skewness      {res} 1.347231
{txt}99%    {res} 74.99999       166.6667       {txt}Kurtosis      {res} 5.496612
{txt}
{com}. matrix survey_matrix[4,1] = 1.75
{txt}
{com}. matrix survey_matrix[4,2] = r(mean)
{txt}
{com}. matrix survey_matrix[4,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[4,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize pct_change if poison_pill==3&wave==1 & online==0, detail

                         {txt}pct_change
{hline 61}
      Percentiles      Smallest
 1%    {res}     -100           -100
{txt} 5%    {res}     -100           -100
{txt}10%    {res}     -100           -100       {txt}Obs         {res}        501
{txt}25%    {res}     -100           -100       {txt}Sum of wgt. {res}        501

{txt}50%    {res}      -40                      {txt}Mean          {res}-37.40186
                        {txt}Largest       Std. dev.     {res} 70.82958
{txt}75%    {res}        0            250
{txt}90%    {res}        0            400       {txt}Variance      {res} 5016.829
{txt}95%    {res} 79.99999            400       {txt}Skewness      {res} 3.526027
{txt}99%    {res}      150            700       {txt}Kurtosis      {res} 30.97972
{txt}
{com}. matrix survey_matrix[5,1] = 2.25
{txt}
{com}. matrix survey_matrix[5,2] = r(mean)
{txt}
{com}. matrix survey_matrix[5,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[5,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize pct_change if poison_pill==1&wave==1 & online==0, detail

                         {txt}pct_change
{hline 61}
      Percentiles      Smallest
 1%    {res}     -100           -100
{txt} 5%    {res}     -100           -100
{txt}10%    {res}     -100           -100       {txt}Obs         {res}        542
{txt}25%    {res}     -100           -100       {txt}Sum of wgt. {res}        542

{txt}50%    {res}     -100                      {txt}Mean          {res}-61.26808
                        {txt}Largest       Std. dev.     {res} 53.05838
{txt}75%    {res}      -30            100
{txt}90%    {res}        0            150       {txt}Variance      {res} 2815.191
{txt}95%    {res}        0            300       {txt}Skewness      {res} 2.488653
{txt}99%    {res}      100            400       {txt}Kurtosis      {res} 16.82141
{txt}
{com}. matrix survey_matrix[6,1] = 2.5
{txt}
{com}. matrix survey_matrix[6,2] = r(mean)
{txt}
{com}. matrix survey_matrix[6,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[6,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize pct_change if poison_pill==2&wave==1 & online==0, detail

                         {txt}pct_change
{hline 61}
      Percentiles      Smallest
 1%    {res}     -100           -100
{txt} 5%    {res}     -100           -100
{txt}10%    {res}     -100           -100       {txt}Obs         {res}        529
{txt}25%    {res}     -100           -100       {txt}Sum of wgt. {res}        529

{txt}50%    {res}     -100                      {txt}Mean          {res}-69.90113
                        {txt}Largest       Std. dev.     {res} 44.96121
{txt}75%    {res}      -50            100
{txt}90%    {res}        0            100       {txt}Variance      {res}  2021.51
{txt}95%    {res}        0            100       {txt}Skewness      {res} 2.036988
{txt}99%    {res} 66.66666            300       {txt}Kurtosis      {res} 11.57007
{txt}
{com}. matrix survey_matrix[7,1] = 2.75
{txt}
{com}. matrix survey_matrix[7,2] = r(mean)
{txt}
{com}. matrix survey_matrix[7,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[7,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize pct_change if poison_pill==4&wave==1 & online==0, detail

                         {txt}pct_change
{hline 61}
      Percentiles      Smallest
 1%    {res}     -100           -100
{txt} 5%    {res}     -100           -100
{txt}10%    {res}     -100           -100       {txt}Obs         {res}        460
{txt}25%    {res}     -100           -100       {txt}Sum of wgt. {res}        460

{txt}50%    {res}     -100                      {txt}Mean          {res}-65.73646
                        {txt}Largest       Std. dev.     {res} 45.88504
{txt}75%    {res}-35.41667            100
{txt}90%    {res}        0            100       {txt}Variance      {res} 2105.437
{txt}95%    {res}        0            100       {txt}Skewness      {res} 1.289428
{txt}99%    {res}      100            150       {txt}Kurtosis      {res} 4.548882
{txt}
{com}. matrix survey_matrix[8,1] = 3
{txt}
{com}. matrix survey_matrix[8,2] = r(mean)
{txt}
{com}. matrix survey_matrix[8,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[8,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize pct_change if poison_pill==3&wave==2 & online==0, detail

                         {txt}pct_change
{hline 61}
      Percentiles      Smallest
 1%    {res}     -100           -100
{txt} 5%    {res}     -100           -100
{txt}10%    {res}     -100           -100       {txt}Obs         {res}        306
{txt}25%    {res}     -100           -100       {txt}Sum of wgt. {res}        306

{txt}50%    {res}      -50                      {txt}Mean          {res}-47.71034
                        {txt}Largest       Std. dev.     {res} 59.61239
{txt}75%    {res}        0       133.3333
{txt}90%    {res}        0            200       {txt}Variance      {res} 3553.637
{txt}95%    {res}       60            200       {txt}Skewness      {res} 1.318526
{txt}99%    {res} 133.3333       233.3333       {txt}Kurtosis      {res} 5.542052
{txt}
{com}. matrix survey_matrix[9,1] = 3.5
{txt}
{com}. matrix survey_matrix[9,2] = r(mean)
{txt}
{com}. matrix survey_matrix[9,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[9,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize pct_change if poison_pill==1&wave==2 & online==0, detail

                         {txt}pct_change
{hline 61}
      Percentiles      Smallest
 1%    {res}     -100           -100
{txt} 5%    {res}     -100           -100
{txt}10%    {res}     -100           -100       {txt}Obs         {res}        293
{txt}25%    {res}     -100           -100       {txt}Sum of wgt. {res}        293

{txt}50%    {res}      -50                      {txt}Mean          {res}-42.70234
                        {txt}Largest       Std. dev.     {res} 84.17964
{txt}75%    {res}        0       166.6667
{txt}90%    {res}        0       233.3333       {txt}Variance      {res} 7086.212
{txt}95%    {res}      100            400       {txt}Skewness      {res} 5.657844
{txt}99%    {res} 233.3333       899.9999       {txt}Kurtosis      {res}  57.7916
{txt}
{com}. matrix survey_matrix[10,1] = 3.75
{txt}
{com}. matrix survey_matrix[10,2] = r(mean)
{txt}
{com}. matrix survey_matrix[10,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[10,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize pct_change if poison_pill==2&wave==2 & online==0, detail

                         {txt}pct_change
{hline 61}
      Percentiles      Smallest
 1%    {res}     -100           -100
{txt} 5%    {res}     -100           -100
{txt}10%    {res}     -100           -100       {txt}Obs         {res}        307
{txt}25%    {res}     -100           -100       {txt}Sum of wgt. {res}        307

{txt}50%    {res}     -100                      {txt}Mean          {res}-72.59746
                        {txt}Largest       Std. dev.     {res} 52.42803
{txt}75%    {res}      -50            100
{txt}90%    {res}        0            100       {txt}Variance      {res} 2748.698
{txt}95%    {res}        0       133.3333       {txt}Skewness      {res} 3.501947
{txt}99%    {res}      100            400       {txt}Kurtosis      {res} 24.89915
{txt}
{com}. matrix survey_matrix[11,1] = 4
{txt}
{com}. matrix survey_matrix[11,2] = r(mean)
{txt}
{com}. matrix survey_matrix[11,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[11,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize pct_change if poison_pill==4&wave==2 & online==0, detail

                         {txt}pct_change
{hline 61}
      Percentiles      Smallest
 1%    {res}     -100           -100
{txt} 5%    {res}     -100           -100
{txt}10%    {res}     -100           -100       {txt}Obs         {res}        267
{txt}25%    {res}     -100           -100       {txt}Sum of wgt. {res}        267

{txt}50%    {res}     -100                      {txt}Mean          {res}-61.13623
                        {txt}Largest       Std. dev.     {res} 62.59682
{txt}75%    {res}-28.57143            100
{txt}90%    {res}        0            150       {txt}Variance      {res} 3918.362
{txt}95%    {res} 16.66666            400       {txt}Skewness      {res} 3.489494
{txt}99%    {res}      150            400       {txt}Kurtosis      {res} 23.63302
{txt}
{com}. matrix survey_matrix[12,1] = 4.25
{txt}
{com}. matrix survey_matrix[12,2] = r(mean)
{txt}
{com}. matrix survey_matrix[12,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[12,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. svmat survey_matrix, names(sm_)
{txt}
{com}. 
. tw (pccapsym sm_3 sm_1 sm_4 sm_1, msymbol(point)) (scatter sm_2 sm_1 if sm_1==1 | sm_1==2.25 | sm_1==3.5, msymbol(O)) (scatter sm_2 sm_1 if sm_1==1.25 | sm_1==2.5 | sm_1==3.75, msymbol(dh)) (scatter sm_2 sm_1 if sm_1==1.5 | sm_1==2.75 | sm_1==4, msymbol(th)) (scatter sm_2 sm_1 if sm_1==1.75 | sm_1==3 | sm_1==4.25, msymbol(sh)) ,  ///
>         xtitle("Survey waves", size(small)) ytitle("% change relative to baseline support", size(small)) xlabel(1.37 "Wave 1 online (July 1-7, 2022)" 2.62 "Wave 1 phone (July 1-12, 2022)" 3.87 "Wave 3 phone (May 26 - June 5, 2023)", angle(45) labsize(small))  ylabel(-20 "-20%" -30 "-30%" -40 "-40%" -50 "-50%" -60 "-60%" -70 "-70%" -80 "-80%" , angle(0) labsize(small)) legend(order( 2 "Let LNR/DNR vote" 3 "Zelensky resigns" 4 "Crimea recognized as Russia" 5 "Never join EU") position(2) bmargin(medium))
{res}{txt}
{com}.         
. quietly graph export figure5.jpg, replace
{txt}
{com}. 
. drop sm_* 
{txt}
{com}.         
.         
. ***FIGURE 6: First-difference estimates of relationship between violence exposure and attitudes, online panel
. 
. *6a: REGRESSING PRINCIPLES OF NEGOTIATION FD ON PCA OF VIOLENCE EXPOSURE
.         
. foreach v in fd_pca_principlesneg0_1 fd_q13_norm0_1 fd_q14_norm0_1 fd_q15_norm0_1 fd_q16_norm0_1 fd_q17_norm0_1 fd_q18_norm0_1 fd_q19_norm0_1 fd_q20_norm0_1{c -(}
{txt}  2{com}.         eststo m`v': reghdfe `v' fd_pca_violexposure0_1, a(oblast_lag) vce(cluster oblast_lag)
{txt}  3{com}.         esttab, beta not 
{txt}  4{com}. {c )-}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       633
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      3.03
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0947
{txt}{col 51}R-squared{col 67}= {res}    0.0281
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0119
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0038
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1233

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0663108{col 36}{space 2} .0381092{col 47}{space 1}    1.74{col 56}{space 3}0.095{col 64}{space 4}-.0123428{col 77}{space 3} .1449644
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .3602758{col 36}{space 2} .0204663{col 47}{space 1}   17.60{col 56}{space 3}0.000{col 64}{space 4} .3180355{col 77}{space 3} .4025161
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{hline 28}
{txt}                      (1)   
{txt}             fd_pca_pri~1   
{txt}{hline 28}
{txt}fd_pca_vio~1{res}        0.062   {txt}
{txt}{hline 28}
{txt}N           {res}          633   {txt}
{txt}{hline 28}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       950
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.00
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.9598
{txt}{col 51}R-squared{col 67}= {res}    0.0231
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0033
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0000
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1509

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q13_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0024565{col 36}{space 2} .0482562{col 47}{space 1}   -0.05{col 56}{space 3}0.960{col 64}{space 4}-.1020524{col 77}{space 3} .0971394
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5010558{col 36}{space 2}   .02591{col 47}{space 1}   19.34{col 56}{space 3}0.000{col 64}{space 4} .4475802{col 77}{space 3} .5545313
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{hline 44}
{txt}                      (1)             (2)   
{txt}             fd_pca_pri~1    fd_q13_nor~1   
{txt}{hline 44}
{txt}fd_pca_vio~1{res}        0.062          -0.002   {txt}
{txt}{hline 44}
{txt}N           {res}          633             950   {txt}
{txt}{hline 44}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       977
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.37
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.5503
{txt}{col 51}R-squared{col 67}= {res}    0.0245
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0012
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0004
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1848

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q14_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0327614{col 36}{space 2} .0540714{col 47}{space 1}   -0.61{col 56}{space 3}0.550{col 64}{space 4}-.1443593{col 77}{space 3} .0788364
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5257993{col 36}{space 2} .0290662{col 47}{space 1}   18.09{col 56}{space 3}0.000{col 64}{space 4} .4658097{col 77}{space 3} .5857889
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{hline 60}
{txt}                      (1)             (2)             (3)   
{txt}             fd_pca_pri~1    fd_q13_nor~1    fd_q14_nor~1   
{txt}{hline 60}
{txt}fd_pca_vio~1{res}        0.062          -0.002          -0.020   {txt}
{txt}{hline 60}
{txt}N           {res}          633             950             977   {txt}
{txt}{hline 60}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,007
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      1.21
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.2830
{txt}{col 51}R-squared{col 67}= {res}    0.0268
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0020
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0014
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1136

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q15_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0376105{col 36}{space 2} .0342474{col 47}{space 1}   -1.10{col 56}{space 3}0.283{col 64}{space 4}-.1082935{col 77}{space 3} .0330726
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5217523{col 36}{space 2} .0183378{col 47}{space 1}   28.45{col 56}{space 3}0.000{col 64}{space 4} .4839049{col 77}{space 3} .5595997
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{hline 76}
{txt}                      (1)             (2)             (3)             (4)   
{txt}             fd_pca_pri~1    fd_q13_nor~1    fd_q14_nor~1    fd_q15_nor~1   
{txt}{hline 76}
{txt}fd_pca_vio~1{res}        0.062          -0.002          -0.020          -0.037   {txt}
{txt}{hline 76}
{txt}N           {res}          633             950             977            1007   {txt}
{txt}{hline 76}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       963
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.74
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.3990
{txt}{col 51}R-squared{col 67}= {res}    0.0139
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0125
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0012
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1193

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q16_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0366527{col 36}{space 2} .0426853{col 47}{space 1}    0.86{col 56}{space 3}0.399{col 64}{space 4}-.0514455{col 77}{space 3} .1247509
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}  .485438{col 36}{space 2} .0228542{col 47}{space 1}   21.24{col 56}{space 3}0.000{col 64}{space 4} .4382693{col 77}{space 3} .5326068
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{hline 92}
{txt}                      (1)             (2)             (3)             (4)             (5)   
{txt}             fd_pca_pri~1    fd_q13_nor~1    fd_q14_nor~1    fd_q15_nor~1    fd_q16_nor~1   
{txt}{hline 92}
{txt}fd_pca_vio~1{res}        0.062          -0.002          -0.020          -0.037           0.035   {txt}
{txt}{hline 92}
{txt}N           {res}          633             950             977            1007             963   {txt}
{txt}{hline 92}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       930
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.62
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.4383
{txt}{col 51}R-squared{col 67}= {res}    0.0325
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0057
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0003
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1469

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q17_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0234277{col 36}{space 2} .0297261{col 47}{space 1}    0.79{col 56}{space 3}0.438{col 64}{space 4}-.0379239{col 77}{space 3} .0847794
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4820065{col 36}{space 2} .0160091{col 47}{space 1}   30.11{col 56}{space 3}0.000{col 64}{space 4} .4489653{col 77}{space 3} .5150478
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{hline 108}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)   
{txt}             fd_pca_pri~1    fd_q13_nor~1    fd_q14_nor~1    fd_q15_nor~1    fd_q16_nor~1    fd_q17_nor~1   
{txt}{hline 108}
{txt}fd_pca_vio~1{res}        0.062          -0.002          -0.020          -0.037           0.035           0.018   {txt}
{txt}{hline 108}
{txt}N           {res}          633             950             977            1007             963             930   {txt}
{txt}{hline 108}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       967
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.04
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.8386
{txt}{col 51}R-squared{col 67}= {res}    0.0152
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0110
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0000
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1600

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q18_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0074948{col 36}{space 2} .0364095{col 47}{space 1}    0.21{col 56}{space 3}0.839{col 64}{space 4}-.0676508{col 77}{space 3} .0826404
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4854949{col 36}{space 2} .0195998{col 47}{space 1}   24.77{col 56}{space 3}0.000{col 64}{space 4}  .445043{col 77}{space 3} .5259468
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{hline 124}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)   
{txt}             fd_pca_pri~1    fd_q13_nor~1    fd_q14_nor~1    fd_q15_nor~1    fd_q16_nor~1    fd_q17_nor~1    fd_q18_nor~1   
{txt}{hline 124}
{txt}fd_pca_vio~1{res}        0.062          -0.002          -0.020          -0.037           0.035           0.018           0.005   {txt}
{txt}{hline 124}
{txt}N           {res}          633             950             977            1007             963             930             967   {txt}
{txt}{hline 124}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       978
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      2.21
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.1504
{txt}{col 51}R-squared{col 67}= {res}    0.0299
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0045
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0046
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1191

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q19_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}   .07161{col 36}{space 2} .0482033{col 47}{space 1}    1.49{col 56}{space 3}0.150{col 64}{space 4}-.0278766{col 77}{space 3} .1710966
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4649296{col 36}{space 2}  .025844{col 47}{space 1}   17.99{col 56}{space 3}0.000{col 64}{space 4} .4115902{col 77}{space 3} .5182691
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{hline 140}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)   
{txt}             fd_pca_pri~1    fd_q13_nor~1    fd_q14_nor~1    fd_q15_nor~1    fd_q16_nor~1    fd_q17_nor~1    fd_q18_nor~1    fd_q19_nor~1   
{txt}{hline 140}
{txt}fd_pca_vio~1{res}        0.062          -0.002          -0.020          -0.037           0.035           0.018           0.005           0.068   {txt}
{txt}{hline 140}
{txt}N           {res}          633             950             977            1007             963             930             967             978   {txt}
{txt}{hline 140}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       901
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      5.32
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0300
{txt}{col 51}R-squared{col 67}= {res}    0.0285
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0007
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0026
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1293

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q20_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0568461{col 36}{space 2} .0246363{col 47}{space 1}    2.31{col 56}{space 3}0.030{col 64}{space 4} .0059993{col 77}{space 3}  .107693
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4731107{col 36}{space 2} .0132768{col 47}{space 1}   35.63{col 56}{space 3}0.000{col 64}{space 4} .4457087{col 77}{space 3} .5005127
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{hline 156}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)   
{txt}             fd_pca_pri~1    fd_q13_nor~1    fd_q14_nor~1    fd_q15_nor~1    fd_q16_nor~1    fd_q17_nor~1    fd_q18_nor~1    fd_q19_nor~1    fd_q20_nor~1   
{txt}{hline 156}
{txt}fd_pca_vio~1{res}        0.062          -0.002          -0.020          -0.037           0.035           0.018           0.005           0.068           0.051*  {txt}
{txt}{hline 156}
{txt}N           {res}          633             950             977            1007             963             930             967             978             901   {txt}
{txt}{hline 156}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001

{com}.         
. coefplot m*, drop(_cons) vertical yline(0, lcolor(red)) levels(95 90) legend(off) ///
> coeflabels(mfd_pca_principlesneg0_1= "PCA principles of neg., FD" mfd_q13_norm0_1= "UKR shouldn't negotiate, FD" mfd_q14_norm0_1= "Morally wrong to sell out, FD" mfd_q15_norm0_1= "RUS cannot be trusted, FD" mfd_q16_norm0_1= "RUS will exploit peace, FD" mfd_q17_norm0_1= "Peace is morally right, FD" mfd_q18_norm0_1= "UKR must make peace, FD" mfd_q19_norm0_1= "UKR must make terr. concessions, FD" mfd_q20_norm0_1= "Strategic to keep negotiating, FD", labsize(vsmall) angle(45)) aseq swapnames
{res}{txt}
{com}. 
. quietly graph export figure6a.jpg, replace
{txt}
{com}. 
. eststo clear
{txt}
{com}. 
. 
. *6b: REGRESSING PEACE TERMS PCA FD ON PCA OF VIOLENCE EXPOSURE
. 
. 
. foreach v in fd_pca_peacecomp0_1 fd_q21_norm0_1 fd_q22_norm0_1 fd_q23_norm0_1 fd_q24_norm0_1 fd_q25_norm0_1 fd_q26_norm0_1 fd_q27_norm0_1 fd_q28_norm0_1 fd_q29_norm0_1{c -(}
{txt}  2{com}.         eststo m`v': reghdfe `v' fd_pca_violexposure0_1, a(oblast_lag) vce(cluster oblast_lag)
{txt}  3{com}. {c )-}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       805
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}     21.80
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0001
{txt}{col 51}R-squared{col 67}= {res}    0.0653
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0353
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0286
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.0686

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1008959{col 36}{space 2} .0216103{col 47}{space 1}    4.67{col 56}{space 3}0.000{col 64}{space 4} .0562944{col 77}{space 3} .1454973
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5244863{col 36}{space 2} .0116147{col 47}{space 1}   45.16{col 56}{space 3}0.000{col 64}{space 4} .5005147{col 77}{space 3} .5484579
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       989
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.74
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.3993
{txt}{col 51}R-squared{col 67}= {res}    0.0200
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0054
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0005
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1414

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q21_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0279501{col 36}{space 2} .0325726{col 47}{space 1}    0.86{col 56}{space 3}0.399{col 64}{space 4}-.0392764{col 77}{space 3} .0951766
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4867704{col 36}{space 2} .0174797{col 47}{space 1}   27.85{col 56}{space 3}0.000{col 64}{space 4} .4506941{col 77}{space 3} .5228466
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       994
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      1.13
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.2980
{txt}{col 51}R-squared{col 67}= {res}    0.0196
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0057
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0008
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1265

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q22_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0320989{col 36}{space 2} .0301712{col 47}{space 1}   -1.06{col 56}{space 3}0.298{col 64}{space 4}-.0943691{col 77}{space 3} .0301713
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5090595{col 36}{space 2} .0162222{col 47}{space 1}   31.38{col 56}{space 3}0.000{col 64}{space 4} .4755785{col 77}{space 3} .5425405
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,042
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.64
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.4304
{txt}{col 51}R-squared{col 67}= {res}    0.0239
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0002
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0005
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1192

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q23_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0228464{col 36}{space 2} .0284836{col 47}{space 1}    0.80{col 56}{space 3}0.430{col 64}{space 4} -.035941{col 77}{space 3} .0816337
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4934898{col 36}{space 2} .0152955{col 47}{space 1}   32.26{col 56}{space 3}0.000{col 64}{space 4} .4619215{col 77}{space 3} .5250582
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,016
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      2.52
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.1252
{txt}{col 51}R-squared{col 67}= {res}    0.0267
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0021
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0036
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.0886

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q24_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0467918{col 36}{space 2} .0294498{col 47}{space 1}    1.59{col 56}{space 3}0.125{col 64}{space 4}-.0139896{col 77}{space 3} .1075731
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4819833{col 36}{space 2} .0157995{col 47}{space 1}   30.51{col 56}{space 3}0.000{col 64}{space 4} .4493746{col 77}{space 3} .5145919
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       998
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      6.65
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0165
{txt}{col 51}R-squared{col 67}= {res}    0.0395
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0148
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0116
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1011

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q25_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0970741{col 36}{space 2} .0376368{col 47}{space 1}    2.58{col 56}{space 3}0.016{col 64}{space 4} .0193957{col 77}{space 3} .1747526
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4508792{col 36}{space 2} .0201713{col 47}{space 1}   22.35{col 56}{space 3}0.000{col 64}{space 4} .4092476{col 77}{space 3} .4925108
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,012
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      4.79
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0385
{txt}{col 51}R-squared{col 67}= {res}    0.0338
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0093
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0083
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.0991

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q26_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0792893{col 36}{space 2}   .03621{col 47}{space 1}    2.19{col 56}{space 3}0.039{col 64}{space 4} .0045554{col 77}{space 3} .1540231
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4617013{col 36}{space 2} .0194534{col 47}{space 1}   23.73{col 56}{space 3}0.000{col 64}{space 4} .4215516{col 77}{space 3} .5018511
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       997
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      1.35
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.2575
{txt}{col 51}R-squared{col 67}= {res}    0.0264
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0013
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0014
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1396

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q27_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0463163{col 36}{space 2} .0399294{col 47}{space 1}    1.16{col 56}{space 3}0.257{col 64}{space 4}-.0360939{col 77}{space 3} .1287266
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4774375{col 36}{space 2} .0213968{col 47}{space 1}   22.31{col 56}{space 3}0.000{col 64}{space 4} .4332767{col 77}{space 3} .5215982
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,011
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      5.16
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0323
{txt}{col 51}R-squared{col 67}= {res}    0.0271
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0024
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0041
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1096

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q28_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0623043{col 36}{space 2} .0274222{col 47}{space 1}    2.27{col 56}{space 3}0.032{col 64}{space 4} .0057075{col 77}{space 3}  .118901
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4771055{col 36}{space 2} .0147131{col 47}{space 1}   32.43{col 56}{space 3}0.000{col 64}{space 4} .4467393{col 77}{space 3} .5074718
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       995
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.85
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.3666
{txt}{col 51}R-squared{col 67}= {res}    0.0254
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0003
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0009
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1045

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        fd_q29_norm0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0267897{col 36}{space 2} .0291115{col 47}{space 1}    0.92{col 56}{space 3}0.367{col 64}{space 4}-.0332934{col 77}{space 3} .0868728
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4837937{col 36}{space 2}  .015645{col 47}{space 1}   30.92{col 56}{space 3}0.000{col 64}{space 4} .4515041{col 77}{space 3} .5160834
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}
{com}.         
. coefplot m*, drop(_cons) vertical yline(0, lcolor(red)) levels(95 90) coeflabels(mfd_pca_peacecomp0_1="PCA peace components, FD" mfd_q21_norm0_1 = "UKR shouldn't join NATO, FD" mfd_q22_norm0_1 = "Western security guarantees, FD" mfd_q23_norm0_1 = "Russian as official language, FD" mfd_q24_norm0_1 = "Crimea as part of Russia, FD" mfd_q25_norm0_1 = "Independent DNR/LNR, FD" mfd_q26_norm0_1 = "Reduce UKR army size, FD" mfd_q27_norm0_1="Voting by DNR/LNR, FD" mfd_q28_norm0_1="UKR reject joining EU, FD" mfd_q29_norm0_1="Zelensky stepping down, FD",  labsize(vsmall) angle(45)) legend(off) aseq swapnames
{res}{txt}
{com}. 
. quietly graph export figure6b.jpg, replace
{txt}
{com}. 
. eststo clear
{txt}
{com}. 
. 
. ***FIGURE 7: First-difference estimates of relationship between violence exposure and attitudes, online panel
. 
. *7a: REGRESSING `STRATEGIC TO KEEP NEGOTIATING' ON COMPONENTS OF VIOLENCE EXPOSURE
. rename fd_shelterinplace_exposure0_1 fd_shelterinplace_exp0_1
{res}{txt}
{com}. 
. foreach v in fd_pca_violexposure0_1 fd_shelling_exposure0_1 fd_bombsiren_exposure0_1 fd_gunfire_exposure0_1 fd_shelterinplace_exp0_1 fd_bombshelter_exposure0_1 fd_vizwounded_exposure0_1 {c -(}
{txt}  2{com}.         eststo m`v': reghdfe fd_q20_norm `v', a(oblast_lag) vce(cluster oblast_lag)
{txt}  3{com}. {c )-}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       901
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      5.32
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0300
{txt}{col 51}R-squared{col 67}= {res}    0.0285
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0007
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0026
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2587

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}           fd_q20_norm{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1136923{col 36}{space 2} .0492726{col 47}{space 1}    2.31{col 56}{space 3}0.030{col 64}{space 4} .0119986{col 77}{space 3}  .215386
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-.0537786{col 36}{space 2} .0265536{col 47}{space 1}   -2.03{col 56}{space 3}0.054{col 64}{space 4}-.1085826{col 77}{space 3} .0010255
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,166
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.00
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.9886
{txt}{col 51}R-squared{col 67}= {res}    0.0225
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0010
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0000
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2617

{txt}{ralign 89:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}            fd_q20_norm{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_shelling_exposure0_1 {c |}{col 25}{res}{space 2}-.0004798{col 37}{space 2} .0333439{col 48}{space 1}   -0.01{col 57}{space 3}0.989{col 65}{space 4}-.0692982{col 78}{space 3} .0683386
{txt}{space 18}_cons {c |}{col 25}{res}{space 2}  .001972{col 37}{space 2} .0178396{col 48}{space 1}    0.11{col 57}{space 3}0.913{col 65}{space 4}-.0348472{col 78}{space 3} .0387912
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,275
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.38
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.5445
{txt}{col 51}R-squared{col 67}= {res}    0.0158
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0039
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0004
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2626

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q20_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_bombsiren_exposure0_1 {c |}{col 26}{res}{space 2}  .038867{col 38}{space 2} .0632249{col 49}{space 1}    0.61{col 58}{space 3}0.545{col 66}{space 4}-.0916228{col 79}{space 3} .1693568
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0164784{col 38}{space 2} .0341414{col 49}{space 1}   -0.48{col 58}{space 3}0.634{col 66}{space 4}-.0869429{col 79}{space 3} .0539861
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,165
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.00
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.9838
{txt}{col 51}R-squared{col 67}= {res}    0.0173
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0042
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0000
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2632

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}           fd_q20_norm{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_gunfire_exposure0_1 {c |}{col 24}{res}{space 2}-.0007321{col 36}{space 2} .0355907{col 47}{space 1}   -0.02{col 56}{space 3}0.984{col 64}{space 4}-.0741876{col 77}{space 3} .0727234
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .0053257{col 36}{space 2} .0189613{col 47}{space 1}    0.28{col 56}{space 3}0.781{col 64}{space 4}-.0338086{col 77}{space 3} .0444599
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,128
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      1.93
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.1774
{txt}{col 51}R-squared{col 67}= {res}    0.0194
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0028
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0013
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2650

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q20_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_shelterinplace_exp0_1 {c |}{col 26}{res}{space 2} .0519255{col 38}{space 2} .0373696{col 49}{space 1}    1.39{col 58}{space 3}0.177{col 66}{space 4}-.0252016{col 79}{space 3} .1290526
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0219965{col 38}{space 2}  .020137{col 49}{space 1}   -1.09{col 58}{space 3}0.286{col 66}{space 4}-.0635571{col 79}{space 3} .0195642
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,158
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.65
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.4280
{txt}{col 51}R-squared{col 67}= {res}    0.0163
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0054
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0006
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2653

{txt}{ralign 92:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}               fd_q20_norm{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      t{col 60}   P>|t|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_bombshelter_exposure0_1 {c |}{col 28}{res}{space 2}  .035045{col 40}{space 2} .0434612{col 51}{space 1}    0.81{col 60}{space 3}0.428{col 68}{space 4}-.0546544{col 81}{space 3} .1247444
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-.0149448{col 40}{space 2} .0238886{col 51}{space 1}   -0.63{col 60}{space 3}0.537{col 68}{space 4}-.0642485{col 81}{space 3} .0343589
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,189
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}     13.92
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0010
{txt}{col 51}R-squared{col 67}= {res}    0.0273
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0064
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0069
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2601

{txt}{ralign 91:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}              fd_q20_norm{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      t{col 59}   P>|t|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_vizwounded_exposure0_1 {c |}{col 27}{res}{space 2}  .141895{col 39}{space 2} .0380251{col 50}{space 1}    3.73{col 59}{space 3}0.001{col 67}{space 4} .0634151{col 80}{space 3} .2203748
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} -.066748{col 39}{space 2}  .019972{col 50}{space 1}   -3.34{col 59}{space 3}0.003{col 67}{space 4}-.1079681{col 80}{space 3}-.0255279
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}
{com}. 
. coefplot m*, drop(_cons) vertical yline(0, lcolor(red) lpattern(dash)) levels(95 90) coeflabels(fd_pca_violexposure0_1 = "PCA viol expos, FD" fd_shelling_exposure0_1 = "Shelling, FD" fd_bombsiren_exposure0_1 = "Siren, FD" fd_gunfire_exposure0_1 = "Gunfire, FD"  fd_shelterinplace_exp0_1 = "Sheltering, FD" fd_bombshelter_exposure0_1 = "Bomb shelter, FD" fd_vizwounded_exposure0_1 = "Seen wounded, FD",  labsize(small) angle(45)) legend(off) graphregion(color(white))
{res}{txt}
{com}. 
. quietly graph export figure7a.jpg, replace
{txt}
{com}. 
. eststo clear 
{txt}
{com}. 
. 
. *7b: REGRESSING `INDEPENDENT DNR/LNR' ON COMPONENTS OF VIOLENCE EXPOSURE
. 
. foreach v in fd_pca_violexposure0_1 fd_shelling_exposure0_1 fd_bombsiren_exposure0_1 fd_gunfire_exposure0_1 fd_shelterinplace_exp0_1 fd_bombshelter_exposure0_1 fd_vizwounded_exposure0_1{c -(}
{txt}  2{com}.         eststo m`v': reghdfe fd_q25_norm `v', a(oblast_lag) vce(cluster oblast_lag)
{txt}  3{com}. {c )-}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       998
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      6.65
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0165
{txt}{col 51}R-squared{col 67}= {res}    0.0395
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0148
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0116
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2022

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}           fd_q25_norm{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1941482{col 36}{space 2} .0752735{col 47}{space 1}    2.58{col 56}{space 3}0.016{col 64}{space 4} .0387913{col 77}{space 3} .3495051
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-.0982416{col 36}{space 2} .0403426{col 47}{space 1}   -2.44{col 56}{space 3}0.023{col 64}{space 4}-.1815047{col 77}{space 3}-.0149785
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,306
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      2.56
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.1226
{txt}{col 51}R-squared{col 67}= {res}    0.0207
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0016
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0022
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2101

{txt}{ralign 89:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}            fd_q25_norm{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_shelling_exposure0_1 {c |}{col 25}{res}{space 2} .0523905{col 37}{space 2} .0327375{col 48}{space 1}    1.60{col 57}{space 3}0.123{col 65}{space 4}-.0151764{col 78}{space 3} .1199575
{txt}{space 18}_cons {c |}{col 25}{res}{space 2}-.0273471{col 37}{space 2} .0173756{col 48}{space 1}   -1.57{col 57}{space 3}0.129{col 65}{space 4}-.0632087{col 78}{space 3} .0085144
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,435
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      1.85
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.1862
{txt}{col 51}R-squared{col 67}= {res}    0.0169
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0006
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0016
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2097

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q25_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_bombsiren_exposure0_1 {c |}{col 26}{res}{space 2} .0596484{col 38}{space 2} .0438301{col 49}{space 1}    1.36{col 58}{space 3}0.186{col 66}{space 4}-.0308124{col 79}{space 3} .1501091
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0304868{col 38}{space 2} .0235797{col 49}{space 1}   -1.29{col 58}{space 3}0.208{col 66}{space 4}-.0791528{col 79}{space 3} .0181792
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,319
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      9.67
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0048
{txt}{col 51}R-squared{col 67}= {res}    0.0220
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0031
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0050
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2073

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}           fd_q25_norm{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_gunfire_exposure0_1 {c |}{col 24}{res}{space 2}  .091298{col 36}{space 2} .0293569{col 47}{space 1}    3.11{col 56}{space 3}0.005{col 64}{space 4} .0307084{col 77}{space 3} .1518875
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-.0463591{col 36}{space 2}  .015565{col 47}{space 1}   -2.98{col 56}{space 3}0.007{col 64}{space 4}-.0784837{col 77}{space 3}-.0142346
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,268
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      3.69
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0666
{txt}{col 51}R-squared{col 67}= {res}    0.0223
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0026
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0019
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2085

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q25_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_shelterinplace_exp0_1 {c |}{col 26}{res}{space 2} .0497686{col 38}{space 2} .0258984{col 49}{space 1}    1.92{col 58}{space 3}0.067{col 66}{space 4}-.0036831{col 79}{space 3} .1032203
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0261632{col 38}{space 2}  .013984{col 49}{space 1}   -1.87{col 58}{space 3}0.074{col 66}{space 4}-.0550248{col 79}{space 3} .0026985
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,302
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      2.81
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.1064
{txt}{col 51}R-squared{col 67}= {res}    0.0243
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0052
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0035
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2056

{txt}{ralign 92:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}               fd_q25_norm{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      t{col 60}   P>|t|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_bombshelter_exposure0_1 {c |}{col 28}{res}{space 2} .0640931{col 40}{space 2}  .038205{col 51}{space 1}    1.68{col 60}{space 3}0.106{col 68}{space 4}-.0147582{col 81}{space 3} .1429445
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-.0370217{col 40}{space 2} .0208778{col 51}{space 1}   -1.77{col 60}{space 3}0.089{col 68}{space 4}-.0801114{col 81}{space 3} .0060679
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,336
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      4.05
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0555
{txt}{col 51}R-squared{col 67}= {res}    0.0178
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0009
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0027
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2127

{txt}{ralign 91:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}              fd_q25_norm{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      t{col 59}   P>|t|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_vizwounded_exposure0_1 {c |}{col 27}{res}{space 2} .0729721{col 39}{space 2} .0362534{col 50}{space 1}    2.01{col 59}{space 3}0.055{col 67}{space 4}-.0018512{col 80}{space 3} .1477955
{txt}{space 20}_cons {c |}{col 27}{res}{space 2}-.0355524{col 39}{space 2} .0189272{col 50}{space 1}   -1.88{col 59}{space 3}0.073{col 67}{space 4}-.0746163{col 80}{space 3} .0035114
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}
{com}. 
. coefplot m*, drop(_cons) vertical yline(0, lcolor(red) lpattern(dash)) levels(95 90) coeflabels(fd_pca_violexposure0_1 = "PCA viol expos, FD" fd_shelling_exposure0_1 = "Shelling, FD" fd_bombsiren_exposure0_1 = "Siren, FD" fd_gunfire_exposure0_1 = "Gunfire, FD" fd_shelterinplace_exp0_1 = "Sheltering, FD" fd_bombshelter_exposure0_1 = "Bomb shelter, FD" fd_vizwounded_exposure0_1 = "Seen wounded, FD",  labsize(small) angle(45)) legend(off) graphregion(color(white))
{res}{txt}
{com}. 
. quietly graph export figure7b.jpg, replace
{txt}
{com}. 
. eststo clear 
{txt}
{com}. 
. 
. ***FIGURE 8: First-difference estimates of relationship between violence exposure and attitudes, online panel
. 
. *8a: REGRESSING `REDUCING UKRAINE'S ARMY' ON COMPONENTS OF VIOLENCE EXPOSURE
. 
. foreach v in fd_pca_violexposure0_1 fd_shelling_exposure0_1 fd_bombsiren_exposure0_1 fd_gunfire_exposure0_1 fd_shelterinplace_exp0_1 fd_bombshelter_exposure0_1 fd_vizwounded_exposure0_1{c -(}
{txt}  2{com}.         eststo m`v': reghdfe fd_q26_norm `v', a(oblast_lag) vce(cluster oblast_lag)
{txt}  3{com}. {c )-}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,012
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      4.79
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0385
{txt}{col 51}R-squared{col 67}= {res}    0.0338
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0093
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0083
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1983

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}           fd_q26_norm{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1585785{col 36}{space 2} .0724201{col 47}{space 1}    2.19{col 56}{space 3}0.039{col 64}{space 4} .0091108{col 77}{space 3} .3080462
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-.0765973{col 36}{space 2} .0389067{col 47}{space 1}   -1.97{col 56}{space 3}0.061{col 64}{space 4}-.1568968{col 77}{space 3} .0037022
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,319
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.33
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.5698
{txt}{col 51}R-squared{col 67}= {res}    0.0216
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0026
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0006
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1961

{txt}{ralign 89:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}            fd_q26_norm{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_shelling_exposure0_1 {c |}{col 25}{res}{space 2} .0252972{col 37}{space 2} .0439033{col 48}{space 1}    0.58{col 57}{space 3}0.570{col 65}{space 4}-.0653147{col 78}{space 3} .1159092
{txt}{space 18}_cons {c |}{col 25}{res}{space 2}-.0100361{col 37}{space 2} .0233385{col 48}{space 1}   -0.43{col 57}{space 3}0.671{col 65}{space 4}-.0582044{col 78}{space 3} .0381323
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,451
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.15
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.7009
{txt}{col 51}R-squared{col 67}= {res}    0.0248
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0077
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0001
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1918

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q26_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_bombsiren_exposure0_1 {c |}{col 26}{res}{space 2} .0119139{col 38}{space 2} .0306476{col 49}{space 1}    0.39{col 58}{space 3}0.701{col 66}{space 4}-.0513397{col 79}{space 3} .0751674
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0004857{col 38}{space 2} .0164961{col 49}{space 1}   -0.03{col 58}{space 3}0.977{col 66}{space 4}-.0345319{col 79}{space 3} .0335605
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,331
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      4.96
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0356
{txt}{col 51}R-squared{col 67}= {res}    0.0298
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0112
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0058
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1982

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}           fd_q26_norm{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_gunfire_exposure0_1 {c |}{col 24}{res}{space 2} .0927799{col 36}{space 2} .0416633{col 47}{space 1}    2.23{col 56}{space 3}0.036{col 64}{space 4} .0067912{col 77}{space 3} .1787687
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-.0426774{col 36}{space 2} .0221672{col 47}{space 1}   -1.93{col 56}{space 3}0.066{col 64}{space 4}-.0884283{col 77}{space 3} .0030734
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,279
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.40
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.5334
{txt}{col 51}R-squared{col 67}= {res}    0.0251
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0056
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0006
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1947

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q26_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_shelterinplace_exp0_1 {c |}{col 26}{res}{space 2} .0254488{col 38}{space 2} .0402732{col 49}{space 1}    0.63{col 58}{space 3}0.533{col 66}{space 4} -.057671{col 79}{space 3} .1085687
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0082827{col 38}{space 2} .0217687{col 49}{space 1}   -0.38{col 58}{space 3}0.707{col 66}{space 4}-.0532112{col 79}{space 3} .0366457
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,315
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      5.55
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0270
{txt}{col 51}R-squared{col 67}= {res}    0.0275
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0086
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0040
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1976

{txt}{ralign 92:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}               fd_q26_norm{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      t{col 60}   P>|t|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_bombshelter_exposure0_1 {c |}{col 28}{res}{space 2} .0651721{col 40}{space 2} .0276644{col 51}{space 1}    2.36{col 60}{space 3}0.027{col 68}{space 4} .0080757{col 81}{space 3} .1222686
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-.0279352{col 40}{space 2} .0151505{col 51}{space 1}   -1.84{col 60}{space 3}0.078{col 68}{space 4}-.0592044{col 81}{space 3}  .003334
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,352
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}     12.04
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0020
{txt}{col 51}R-squared{col 67}= {res}    0.0363
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0181
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0127
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1978

{txt}{ralign 91:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}              fd_q26_norm{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      t{col 59}   P>|t|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_vizwounded_exposure0_1 {c |}{col 27}{res}{space 2} .1486572{col 39}{space 2} .0428444{col 50}{space 1}    3.47{col 59}{space 3}0.002{col 67}{space 4} .0602306{col 80}{space 3} .2370837
{txt}{space 20}_cons {c |}{col 27}{res}{space 2}-.0717807{col 39}{space 2} .0224785{col 50}{space 1}   -3.19{col 59}{space 3}0.004{col 67}{space 4}-.1181741{col 80}{space 3}-.0253873
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}
{com}. 
. coefplot m*, drop(_cons) vertical yline(0, lcolor(red) lpattern(dash)) levels(95 90) coeflabels(fd_pca_violexposure0_1 = "PCA viol expos, FD" fd_shelling_exposure0_1 = "Shelling, FD" fd_bombsiren_exposure0_1 = "Siren, FD" fd_gunfire_exposure0_1 = "Gunfire, FD" fd_shelterinplace_exp0_1 = "Sheltering, FD" fd_bombshelter_exposure0_1 = "Bomb shelter, FD" fd_vizwounded_exposure0_1 = "Seen wounded, FD", labsize(small) angle(45)) legend(off) graphregion(color(white))
{res}{txt}
{com}. 
. quietly graph export figure8a.jpg, replace
{txt}
{com}. 
. eststo clear 
{txt}
{com}. 
. *8b: REGRESSING `UKRAINE REJECTS JOINING THE EU' ON COMPONENTS OF VIOLENCE EXPOSURE
. 
. foreach v in fd_pca_violexposure0_1 fd_shelling_exposure0_1 fd_bombsiren_exposure0_1 fd_gunfire_exposure0_1 fd_shelterinplace_exp0_1 fd_bombshelter_exposure0_1 fd_vizwounded_exposure0_1{c -(}
{txt}  2{com}.         eststo m`v': reghdfe fd_q28_norm `v', a(oblast_lag) vce(cluster oblast_lag)
{txt}  3{com}. {c )-}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,011
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      5.16
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0323
{txt}{col 51}R-squared{col 67}= {res}    0.0271
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0024
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0041
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2192

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}           fd_q28_norm{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1246086{col 36}{space 2} .0548445{col 47}{space 1}    2.27{col 56}{space 3}0.032{col 64}{space 4} .0114151{col 77}{space 3}  .237802
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-.0457889{col 36}{space 2} .0294261{col 47}{space 1}   -1.56{col 56}{space 3}0.133{col 64}{space 4}-.1065215{col 77}{space 3} .0149436
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,322
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.12
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.7273
{txt}{col 51}R-squared{col 67}= {res}    0.0223
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0034
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0001
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2267

{txt}{ralign 89:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}            fd_q28_norm{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_shelling_exposure0_1 {c |}{col 25}{res}{space 2} .0100513{col 37}{space 2} .0284886{col 48}{space 1}    0.35{col 57}{space 3}0.727{col 65}{space 4}-.0487463{col 78}{space 3} .0688489
{txt}{space 18}_cons {c |}{col 25}{res}{space 2} .0079847{col 37}{space 2} .0151027{col 48}{space 1}    0.53{col 57}{space 3}0.602{col 65}{space 4}-.0231858{col 78}{space 3} .0391551
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,449
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.66
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.4229
{txt}{col 51}R-squared{col 67}= {res}    0.0214
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0042
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0006
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2246

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q28_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_bombsiren_exposure0_1 {c |}{col 26}{res}{space 2}-.0408481{col 38}{space 2} .0500945{col 49}{space 1}   -0.82{col 58}{space 3}0.423{col 66}{space 4}-.1442381{col 79}{space 3} .0625419
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} .0346917{col 38}{space 2} .0269718{col 49}{space 1}    1.29{col 58}{space 3}0.211{col 66}{space 4}-.0209752{col 79}{space 3} .0903587
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,331
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      8.16
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0087
{txt}{col 51}R-squared{col 67}= {res}    0.0263
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0076
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0043
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2238

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}           fd_q28_norm{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_gunfire_exposure0_1 {c |}{col 24}{res}{space 2} .0913149{col 36}{space 2} .0319709{col 47}{space 1}    2.86{col 56}{space 3}0.009{col 64}{space 4} .0253303{col 77}{space 3} .1572995
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-.0294065{col 36}{space 2} .0168982{col 47}{space 1}   -1.74{col 56}{space 3}0.095{col 64}{space 4}-.0642826{col 77}{space 3} .0054697
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,283
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.02
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.8937
{txt}{col 51}R-squared{col 67}= {res}    0.0186
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0009
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0000
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2214

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q28_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_shelterinplace_exp0_1 {c |}{col 26}{res}{space 2}  .004621{col 38}{space 2} .0342217{col 49}{space 1}    0.14{col 58}{space 3}0.894{col 66}{space 4}-.0660091{col 79}{space 3} .0752512
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} .0135625{col 38}{space 2} .0184667{col 49}{space 1}    0.73{col 58}{space 3}0.470{col 66}{space 4} -.024551{col 79}{space 3}  .051676
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,318
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      3.60
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0699
{txt}{col 51}R-squared{col 67}= {res}    0.0181
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0009
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0010
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2250

{txt}{ralign 92:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}               fd_q28_norm{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      t{col 60}   P>|t|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_bombshelter_exposure0_1 {c |}{col 28}{res}{space 2} .0383987{col 40}{space 2} .0202388{col 51}{space 1}    1.90{col 60}{space 3}0.070{col 68}{space 4}-.0033722{col 81}{space 3} .0801696
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-.0071902{col 40}{space 2}  .011028{col 51}{space 1}   -0.65{col 60}{space 3}0.521{col 68}{space 4}-.0299508{col 81}{space 3} .0155704
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,357
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      3.78
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0636
{txt}{col 51}R-squared{col 67}= {res}    0.0227
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0044
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0017
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2274

{txt}{ralign 91:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}              fd_q28_norm{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      t{col 59}   P>|t|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_vizwounded_exposure0_1 {c |}{col 27}{res}{space 2} .0617402{col 39}{space 2} .0317513{col 50}{space 1}    1.94{col 59}{space 3}0.064{col 67}{space 4}-.0037913{col 80}{space 3} .1272717
{txt}{space 20}_cons {c |}{col 27}{res}{space 2}-.0169547{col 39}{space 2}   .01664{col 50}{space 1}   -1.02{col 59}{space 3}0.318{col 67}{space 4} -.051298{col 80}{space 3} .0173886
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}
{com}. 
. coefplot m*, drop(_cons) vertical yline(0, lcolor(red) lpattern(dash)) levels(95 90) coeflabels(fd_pca_violexposure0_1 = "PCA viol expos, FD" fd_shelling_exposure0_1 = "Shelling, FD" fd_bombsiren_exposure0_1 = "Siren, FD" fd_gunfire_exposure0_1 = "Gunfire, FD" fd_shelterinplace_exp0_1 = "Sheltering, FD" fd_bombshelter_exposure0_1 = "Bomb shelter, FD" fd_vizwounded_exposure0_1 = "Seen wounded, FD", labsize(small) angle(45)) legend(off) graphregion(color(white))
{res}{txt}
{com}. 
. quietly graph export figure8b.jpg, replace
{txt}
{com}. 
. eststo clear 
{txt}
{com}. 
. 
. ***FIGURE 9: Exposure to violence and level of support for an agreement (May 2023 survey)
. *Use q3 and q4 in omnibus to examine whether having close family or friends that have..."
. *Use above_median_expos (based on hearing gunfire) to examine exposure to violence
. 
. preserve
{txt}
{com}. 
. keep if telephone==1 & wave==2
{txt}(6,474 observations deleted)

{com}. 
. tab q3 q4 

        {txt}3. {c |}
  Exposure {c |}
        to {c |}
 Violence. {c |}
  How many {c |}
     close {c |}
 family or {c |}
friends do {c |}
  you know {c |}                                                                      4. Exposure to Violence. How many close family or friends do you know that have 
that have  {c |}         0          1          2          3          4          5          6          7          8          9         10         11         12         14         15         16         17         18         19         20 {c |}     Total
{hline 11}{c +}{hline 220}{c +}{hline 10}
         0 {c |}{res}       302         81         38         31         10         19          6          2          2          1         10          0          1          1          4          0          0          0          1          3 {txt}{c |}{res}       537 
{txt}         1 {c |}{res}        60         31         20         18          7          9          0          1          1          0          9          0          0          0          1          0          0          0          0          2 {txt}{c |}{res}       172 
{txt}         2 {c |}{res}        63         39         29         10          8          8          1          2          1          0          4          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}       171 
{txt}         3 {c |}{res}        46         44         41         10          7          9          3          2          0          0          5          0          1          0          1          0          0          0          0          3 {txt}{c |}{res}       175 
{txt}         4 {c |}{res}        16          9         16         10          2          7          2          3          1          0          6          0          0          0          2          0          0          0          0          1 {txt}{c |}{res}        76 
{txt}         5 {c |}{res}        47         10         35         32         10          5          2          4          2          0         12          1          0          0          3          0          0          1          0          2 {txt}{c |}{res}       173 
{txt}         6 {c |}{res}         5          3          8          5          4          1          3          1          2          0          1          0          1          0          0          0          0          0          0          0 {txt}{c |}{res}        35 
{txt}         7 {c |}{res}         4          2          6          5          1          0          1          1          1          0          1          0          0          0          1          0          0          0          0          0 {txt}{c |}{res}        23 
{txt}         8 {c |}{res}         2          0          2          3          2          3          3          0          0          0          1          1          0          0          0          0          0          0          0          0 {txt}{c |}{res}        17 
{txt}         9 {c |}{res}         0          1          0          2          0          1          1          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         6 
{txt}        10 {c |}{res}        26         16         22         24         17         29          6         10          5          0         31          1          2          1          3          0          0          1          0          9 {txt}{c |}{res}       213 
{txt}        11 {c |}{res}         0          0          0          2          1          1          0          1          0          1          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         7 
{txt}        12 {c |}{res}         1          1          0          2          3          1          1          1          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        10 
{txt}        13 {c |}{res}         0          0          0          0          1          0          0          1          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         2 
{txt}        14 {c |}{res}         1          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        15 {c |}{res}         6          2          1          7          2          5          0          1          1          0          6          1          0          0          2          0          0          0          0          1 {txt}{c |}{res}        38 
{txt}        18 {c |}{res}         1          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        20 {c |}{res}         2          0          2          5          3         11          1          3          5          0         15          1          1          0          2          1          0          0          0          4 {txt}{c |}{res}        63 
{txt}        23 {c |}{res}         0          0          0          1          0          0          0          0          1          0          1          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         3 
{txt}        25 {c |}{res}         1          0          0          0          1          1          0          1          0          0          1          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         5 
{txt}        30 {c |}{res}         2          0          1          0          3          1          2          1          1          0          5          0          1          1          1          0          1          0          0          2 {txt}{c |}{res}        27 
{txt}        35 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          1          0          0          0          0          0 {txt}{c |}{res}         2 
{txt}        40 {c |}{res}         1          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          1 {txt}{c |}{res}         4 
{txt}        42 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          1          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        45 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          1 {txt}{c |}{res}         2 
{txt}        50 {c |}{res}         1          0          0          1          0          1          1          1          1          0          6          0          0          0          2          0          0          0          0          0 {txt}{c |}{res}        22 
{txt}        55 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        60 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         2 
{txt}        70 {c |}{res}         0          0          1          0          0          0          0          0          0          0          0          0          0          0          1          0          0          0          0          0 {txt}{c |}{res}         2 
{txt}        80 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        86 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}       100 {c |}{res}         1          0          0          1          0          1          0          0          0          0          2          0          0          0          0          0          0          0          0          5 {txt}{c |}{res}        21 
{txt}       120 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          1          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}       200 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          1          0          0          0          0          1 {txt}{c |}{res}         5 
{txt}       500 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}       600 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}       760 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}       800 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        DK {c |}{res}        25          9          8         11          3         12          5          0          1          0         13          0          0          0          1          0          0          0          0          2 {txt}{c |}{res}       167 
{txt}        RA {c |}{res}         2          1          0          0          0          1          0          0          0          0          1          0          0          0          0          0          0          0          0          1 {txt}{c |}{res}        22 
{txt}{hline 11}{c +}{hline 220}{c +}{hline 10}
     Total {c |}{res}       615        249        230        180         85        126         38         36         25          2        130          5          8          4         26          1          1          2          1         38 {txt}{c |}{res}     2,013 


        {txt}3. {c |}
  Exposure {c |}
        to {c |}
 Violence. {c |}
  How many {c |}
     close {c |}
 family or {c |}
friends do {c |}
  you know {c |}                                                                      4. Exposure to Violence. How many close family or friends do you know that have 
that have  {c |}        21         23         25         26         30         32         39         40         45         50         56         60         70         80        100        150        200        400         DK         RA {c |}     Total
{hline 11}{c +}{hline 220}{c +}{hline 10}
         0 {c |}{res}         0          0          0          0          2          1          0          1          0          2          0          0          0          0          0          0          0          0         18          1 {txt}{c |}{res}       537 
{txt}         1 {c |}{res}         0          1          0          1          1          0          0          0          0          0          0          0          1          0          0          0          0          0          9          0 {txt}{c |}{res}       172 
{txt}         2 {c |}{res}         0          0          0          0          2          0          0          0          0          0          0          0          0          0          0          0          0          0          4          0 {txt}{c |}{res}       171 
{txt}         3 {c |}{res}         0          0          0          0          0          0          0          0          0          1          0          0          0          0          0          0          0          0          2          0 {txt}{c |}{res}       175 
{txt}         4 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          1          0 {txt}{c |}{res}        76 
{txt}         5 {c |}{res}         0          0          0          0          1          0          0          0          0          2          0          0          0          0          0          0          1          0          3          0 {txt}{c |}{res}       173 
{txt}         6 {c |}{res}         0          1          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        35 
{txt}         7 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        23 
{txt}         8 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        17 
{txt}         9 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          1          0 {txt}{c |}{res}         6 
{txt}        10 {c |}{res}         0          0          0          0          3          0          0          0          0          1          0          0          0          0          0          0          0          0          6          0 {txt}{c |}{res}       213 
{txt}        11 {c |}{res}         0          1          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         7 
{txt}        12 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        10 
{txt}        13 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         2 
{txt}        14 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        15 {c |}{res}         0          0          1          0          1          0          0          0          0          0          0          0          0          0          1          0          0          0          0          0 {txt}{c |}{res}        38 
{txt}        18 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        20 {c |}{res}         1          0          1          0          1          0          0          1          0          3          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        63 
{txt}        23 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         3 
{txt}        25 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         5 
{txt}        30 {c |}{res}         0          0          0          0          2          0          0          0          0          1          0          1          0          0          0          0          0          0          1          0 {txt}{c |}{res}        27 
{txt}        35 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          1          0 {txt}{c |}{res}         2 
{txt}        40 {c |}{res}         0          0          1          0          1          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         4 
{txt}        42 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        45 {c |}{res}         0          0          0          0          0          0          0          0          1          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         2 
{txt}        50 {c |}{res}         0          0          3          0          0          0          0          0          0          2          0          0          0          0          1          0          0          0          2          0 {txt}{c |}{res}        22 
{txt}        55 {c |}{res}         0          0          0          0          0          0          0          0          0          1          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        60 {c |}{res}         0          0          0          0          1          0          1          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         2 
{txt}        70 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         2 
{txt}        80 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          1          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        86 {c |}{res}         0          0          0          0          0          0          0          0          1          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}       100 {c |}{res}         0          0          0          0          1          0          0          1          0          3          1          0          0          0          4          0          0          0          1          0 {txt}{c |}{res}        21 
{txt}       120 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}       200 {c |}{res}         0          0          0          0          0          0          0          1          0          0          0          0          1          0          1          0          0          0          0          0 {txt}{c |}{res}         5 
{txt}       500 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          1          0          0          0          0 {txt}{c |}{res}         1 
{txt}       600 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          1          0          0 {txt}{c |}{res}         1 
{txt}       760 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          1          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}       800 {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          1          0          0          0          0 {txt}{c |}{res}         1 
{txt}        DK {c |}{res}         0          0          0          0          3          0          0          1          0          0          0          0          0          0          0          0          0          0         69          3 {txt}{c |}{res}       167 
{txt}        RA {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0          0         16 {txt}{c |}{res}        22 
{txt}{hline 11}{c +}{hline 220}{c +}{hline 10}
     Total {c |}{res}         1          3          6          1         19          1          1          5          2         16          1          1          2          1          8          2          1          1        118         20 {txt}{c |}{res}     2,013 


           {txt}{c |}     4.
        3. {c |}  Exposure
  Exposure {c |}     to
        to {c |} Violence.
 Violence. {c |}  How many
  How many {c |}   close
     close {c |} family or
 family or {c |} friends do
friends do {c |}  you know
  you know {c |} that have 
that have  {c |}      1000 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}         0 {txt}{c |}{res}       537 
{txt}         1 {c |}{res}         0 {txt}{c |}{res}       172 
{txt}         2 {c |}{res}         0 {txt}{c |}{res}       171 
{txt}         3 {c |}{res}         0 {txt}{c |}{res}       175 
{txt}         4 {c |}{res}         0 {txt}{c |}{res}        76 
{txt}         5 {c |}{res}         0 {txt}{c |}{res}       173 
{txt}         6 {c |}{res}         0 {txt}{c |}{res}        35 
{txt}         7 {c |}{res}         0 {txt}{c |}{res}        23 
{txt}         8 {c |}{res}         0 {txt}{c |}{res}        17 
{txt}         9 {c |}{res}         0 {txt}{c |}{res}         6 
{txt}        10 {c |}{res}         0 {txt}{c |}{res}       213 
{txt}        11 {c |}{res}         0 {txt}{c |}{res}         7 
{txt}        12 {c |}{res}         0 {txt}{c |}{res}        10 
{txt}        13 {c |}{res}         0 {txt}{c |}{res}         2 
{txt}        14 {c |}{res}         0 {txt}{c |}{res}         1 
{txt}        15 {c |}{res}         0 {txt}{c |}{res}        38 
{txt}        18 {c |}{res}         0 {txt}{c |}{res}         1 
{txt}        20 {c |}{res}         0 {txt}{c |}{res}        63 
{txt}        23 {c |}{res}         0 {txt}{c |}{res}         3 
{txt}        25 {c |}{res}         0 {txt}{c |}{res}         5 
{txt}        30 {c |}{res}         0 {txt}{c |}{res}        27 
{txt}        35 {c |}{res}         0 {txt}{c |}{res}         2 
{txt}        40 {c |}{res}         0 {txt}{c |}{res}         4 
{txt}        42 {c |}{res}         0 {txt}{c |}{res}         1 
{txt}        45 {c |}{res}         0 {txt}{c |}{res}         2 
{txt}        50 {c |}{res}         0 {txt}{c |}{res}        22 
{txt}        55 {c |}{res}         0 {txt}{c |}{res}         1 
{txt}        60 {c |}{res}         0 {txt}{c |}{res}         2 
{txt}        70 {c |}{res}         0 {txt}{c |}{res}         2 
{txt}        80 {c |}{res}         0 {txt}{c |}{res}         1 
{txt}        86 {c |}{res}         0 {txt}{c |}{res}         1 
{txt}       100 {c |}{res}         0 {txt}{c |}{res}        21 
{txt}       120 {c |}{res}         0 {txt}{c |}{res}         1 
{txt}       200 {c |}{res}         0 {txt}{c |}{res}         5 
{txt}       500 {c |}{res}         0 {txt}{c |}{res}         1 
{txt}       600 {c |}{res}         0 {txt}{c |}{res}         1 
{txt}       760 {c |}{res}         0 {txt}{c |}{res}         1 
{txt}       800 {c |}{res}         0 {txt}{c |}{res}         1 
{txt}        DK {c |}{res}         1 {txt}{c |}{res}       167 
{txt}        RA {c |}{res}         0 {txt}{c |}{res}        22 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}         1 {txt}{c |}{res}     2,013 
{txt}
{com}. gen q3_new=q3
{txt}
{com}. gen q4_new=q4
{txt}
{com}. 
. replace q3_new=. if q3_new==998 | q3_new==999 // RA or DK answers
{txt}(189 real changes made, 189 to missing)

{com}. replace q4_new=. if q4_new==998 | q4_new==999 // RA or DK answers
{txt}(138 real changes made, 138 to missing)

{com}. 
. sum q3_new q4_new, de

                           {txt}q3_new
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,824
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,824

{txt}50%    {res}        3                      {txt}Mean          {res} 8.668311
                        {txt}Largest       Std. dev.     {res} 35.65522
{txt}75%    {res}        8            500
{txt}90%    {res}       15            600       {txt}Variance      {res} 1271.295
{txt}95%    {res}       30            760       {txt}Skewness      {res} 15.74634
{txt}99%    {res}      100            800       {txt}Kurtosis      {res} 305.5832

                           {txt}q4_new
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,875
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,875

{txt}50%    {res}        2                      {txt}Mean          {res}    5.704
                        {txt}Largest       Std. dev.     {res} 27.48259
{txt}75%    {res}        5            150
{txt}90%    {res}       10            200       {txt}Variance      {res} 755.2928
{txt}95%    {res}       20            400       {txt}Skewness      {res} 27.45453
{txt}99%    {res}       50           1000       {txt}Kurtosis      {res} 940.2462
{txt}
{com}. 
. gen above_med_exposure1=.
{txt}(2,013 missing values generated)

{com}. replace above_med_exposure1=0 if q3_new<3 & q3_new!=.
{txt}(880 real changes made)

{com}. replace above_med_exposure1=1 if q3_new>=3 & q3_new!=.
{txt}(944 real changes made)

{com}. 
. gen above_med_exposure2=.
{txt}(2,013 missing values generated)

{com}. replace above_med_exposure2=0 if q4_new<2 & q4_new!=.
{txt}(864 real changes made)

{com}. replace above_med_exposure2=1 if q4_new>=2 & q4_new!=.
{txt}(1,011 real changes made)

{com}. 
. eststo clear
{txt}
{com}. 
. eststo m1: reg baseline_support above_med_exposure1 female age3039 age4049 age5059 age6069 age70 higher_educ russian_speaker i.oblast_feb24, vce(cluster oblast_feb24)

{txt}Linear regression                               Number of obs     = {res}     1,687
                                                {txt}{help j_robustsingular:F(8, 24) }         =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0704
                                                {txt}Root MSE          =    {res} .41073

{txt}{ralign 89:(Std. err. adjusted for {res:25} clusters in {res:oblast_feb24})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}       baseline_support{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}above_med_exposure1 {c |}{col 25}{res}{space 2}-.0225894{col 37}{space 2} .0160346{col 48}{space 1}   -1.41{col 57}{space 3}0.172{col 65}{space 4}-.0556831{col 78}{space 3} .0105043
{txt}{space 17}female {c |}{col 25}{res}{space 2}   .07759{col 37}{space 2}  .014348{col 48}{space 1}    5.41{col 57}{space 3}0.000{col 65}{space 4} .0479772{col 78}{space 3} .1072027
{txt}{space 16}age3039 {c |}{col 25}{res}{space 2} .0020018{col 37}{space 2}  .022551{col 48}{space 1}    0.09{col 57}{space 3}0.930{col 65}{space 4}-.0445412{col 78}{space 3} .0485448
{txt}{space 16}age4049 {c |}{col 25}{res}{space 2}-.0708967{col 37}{space 2}  .026067{col 48}{space 1}   -2.72{col 57}{space 3}0.012{col 65}{space 4}-.1246964{col 78}{space 3}-.0170971
{txt}{space 16}age5059 {c |}{col 25}{res}{space 2}-.1355837{col 37}{space 2} .0334494{col 48}{space 1}   -4.05{col 57}{space 3}0.000{col 65}{space 4}  -.20462{col 78}{space 3}-.0665475
{txt}{space 16}age6069 {c |}{col 25}{res}{space 2}-.0521968{col 37}{space 2} .0226222{col 48}{space 1}   -2.31{col 57}{space 3}0.030{col 65}{space 4}-.0988867{col 78}{space 3} -.005507
{txt}{space 18}age70 {c |}{col 25}{res}{space 2} .0339304{col 37}{space 2} .0311702{col 48}{space 1}    1.09{col 57}{space 3}0.287{col 65}{space 4}-.0304017{col 78}{space 3} .0982624
{txt}{space 12}higher_educ {c |}{col 25}{res}{space 2}-.1021465{col 37}{space 2} .0188089{col 48}{space 1}   -5.43{col 57}{space 3}0.000{col 65}{space 4}-.1409661{col 78}{space 3}-.0633269
{txt}{space 8}russian_speaker {c |}{col 25}{res}{space 2} .0727834{col 37}{space 2} .0277644{col 48}{space 1}    2.62{col 57}{space 3}0.015{col 65}{space 4} .0154805{col 78}{space 3} .1300863
{txt}{space 23} {c |}
{space 11}oblast_feb24 {c |}
{space 11}Kyiv oblast  {c |}{col 25}{res}{space 2} .0887087{col 37}{space 2} .0055736{col 48}{space 1}   15.92{col 57}{space 3}0.000{col 65}{space 4} .0772054{col 78}{space 3}  .100212
{txt}{space 6}Vinnytsya oblast  {c |}{col 25}{res}{space 2}   .00416{col 37}{space 2} .0069215{col 48}{space 1}    0.60{col 57}{space 3}0.553{col 65}{space 4}-.0101251{col 78}{space 3} .0184452
{txt}{space 10}Volyn oblast  {c |}{col 25}{res}{space 2} .0473819{col 37}{space 2} .0095213{col 48}{space 1}    4.98{col 57}{space 3}0.000{col 65}{space 4}  .027731{col 78}{space 3} .0670328
{txt}{space 1}Dnipropetrovsk oblast  {c |}{col 25}{res}{space 2} .1324751{col 37}{space 2} .0044896{col 48}{space 1}   29.51{col 57}{space 3}0.000{col 65}{space 4} .1232091{col 78}{space 3} .1417411
{txt}{space 8}Donetsk oblast  {c |}{col 25}{res}{space 2} .1762147{col 37}{space 2} .0113816{col 48}{space 1}   15.48{col 57}{space 3}0.000{col 65}{space 4} .1527242{col 78}{space 3} .1997052
{txt}{space 7}Zhytomyr oblast  {c |}{col 25}{res}{space 2} .1467786{col 37}{space 2} .0093724{col 48}{space 1}   15.66{col 57}{space 3}0.000{col 65}{space 4}  .127435{col 78}{space 3} .1661222
{txt}{space 4}Zakarpattya oblast  {c |}{col 25}{res}{space 2}  .083882{col 37}{space 2} .0081162{col 48}{space 1}   10.34{col 57}{space 3}0.000{col 65}{space 4} .0671309{col 78}{space 3} .1006331
{txt}{space 3}Zaporizhzhya oblast  {c |}{col 25}{res}{space 2}  .087786{col 37}{space 2} .0045575{col 48}{space 1}   19.26{col 57}{space 3}0.000{col 65}{space 4} .0783798{col 78}{space 3} .0971923
{txt}Ivano-Frankivsk oblast  {c |}{col 25}{res}{space 2} .0619574{col 37}{space 2} .0071964{col 48}{space 1}    8.61{col 57}{space 3}0.000{col 65}{space 4} .0471048{col 78}{space 3} .0768101
{txt}{space 5}Kirovohrad oblast  {c |}{col 25}{res}{space 2} .1452056{col 37}{space 2} .0113016{col 48}{space 1}   12.85{col 57}{space 3}0.000{col 65}{space 4} .1218803{col 78}{space 3} .1685309
{txt}{space 8}Luhansk oblast  {c |}{col 25}{res}{space 2}-.1698524{col 37}{space 2} .0053194{col 48}{space 1}  -31.93{col 57}{space 3}0.000{col 65}{space 4} -.180831{col 78}{space 3}-.1588737
{txt}{space 11}Lviv oblast  {c |}{col 25}{res}{space 2} .0654478{col 37}{space 2} .0066439{col 48}{space 1}    9.85{col 57}{space 3}0.000{col 65}{space 4} .0517354{col 78}{space 3} .0791602
{txt}{space 6}Mykolayiv oblast  {c |}{col 25}{res}{space 2} .1765406{col 37}{space 2} .0046432{col 48}{space 1}   38.02{col 57}{space 3}0.000{col 65}{space 4} .1669575{col 78}{space 3} .1861236
{txt}{space 10}Odesa oblast  {c |}{col 25}{res}{space 2} .0912274{col 37}{space 2} .0061899{col 48}{space 1}   14.74{col 57}{space 3}0.000{col 65}{space 4} .0784521{col 78}{space 3} .1040028
{txt}{space 8}Poltava oblast  {c |}{col 25}{res}{space 2}  .077237{col 37}{space 2} .0045428{col 48}{space 1}   17.00{col 57}{space 3}0.000{col 65}{space 4} .0678612{col 78}{space 3} .0866128
{txt}{space 10}Rivne oblast  {c |}{col 25}{res}{space 2}-.0157786{col 37}{space 2} .0077106{col 48}{space 1}   -2.05{col 57}{space 3}0.052{col 65}{space 4}-.0316925{col 78}{space 3} .0001353
{txt}{space 11}Sumy oblast  {c |}{col 25}{res}{space 2} .1186514{col 37}{space 2} .0080329{col 48}{space 1}   14.77{col 57}{space 3}0.000{col 65}{space 4} .1020724{col 78}{space 3} .1352304
{txt}{space 7}Ternopil oblast  {c |}{col 25}{res}{space 2}-.0644557{col 37}{space 2} .0085816{col 48}{space 1}   -7.51{col 57}{space 3}0.000{col 65}{space 4}-.0821673{col 78}{space 3}-.0467442
{txt}{space 8}Kharkiv oblast  {c |}{col 25}{res}{space 2} .0856708{col 37}{space 2} .0071787{col 48}{space 1}   11.93{col 57}{space 3}0.000{col 65}{space 4} .0708547{col 78}{space 3} .1004868
{txt}{space 8}Kherson oblast  {c |}{col 25}{res}{space 2} .0845552{col 37}{space 2} .0057642{col 48}{space 1}   14.67{col 57}{space 3}0.000{col 65}{space 4} .0726585{col 78}{space 3} .0964519
{txt}{space 3}Khmelnytskiy oblast  {c |}{col 25}{res}{space 2}  .052735{col 37}{space 2} .0068039{col 48}{space 1}    7.75{col 57}{space 3}0.000{col 65}{space 4} .0386924{col 78}{space 3} .0667776
{txt}{space 7}Cherkasy oblast  {c |}{col 25}{res}{space 2} .0193393{col 37}{space 2} .0104352{col 48}{space 1}    1.85{col 57}{space 3}0.076{col 65}{space 4}-.0021978{col 78}{space 3} .0408764
{txt}{space 5}Chernivtsi oblast  {c |}{col 25}{res}{space 2} .2291126{col 37}{space 2} .0080055{col 48}{space 1}   28.62{col 57}{space 3}0.000{col 65}{space 4} .2125901{col 78}{space 3} .2456351
{txt}{space 6}Chernihiv oblast  {c |}{col 25}{res}{space 2}-.0156374{col 37}{space 2} .0055982{col 48}{space 1}   -2.79{col 57}{space 3}0.010{col 65}{space 4}-.0271914{col 78}{space 3}-.0040834
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .4664645{col 37}{space 2} .0178686{col 48}{space 1}   26.11{col 57}{space 3}0.000{col 65}{space 4} .4295855{col 78}{space 3} .5033435
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo m2: reg baseline_support above_med_exposure2 female age3039 age4049 age5059 age6069 age70 higher_educ russian_speaker i.oblast_feb24, vce(cluster oblast_feb24)

{txt}Linear regression                               Number of obs     = {res}     1,734
                                                {txt}{help j_robustsingular:F(8, 24) }         =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0681
                                                {txt}Root MSE          =    {res} .41165

{txt}{ralign 89:(Std. err. adjusted for {res:25} clusters in {res:oblast_feb24})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}       baseline_support{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}above_med_exposure2 {c |}{col 25}{res}{space 2}-.0557412{col 37}{space 2}  .018043{col 48}{space 1}   -3.09{col 57}{space 3}0.005{col 65}{space 4}  -.09298{col 78}{space 3}-.0185023
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0794112{col 37}{space 2} .0142407{col 48}{space 1}    5.58{col 57}{space 3}0.000{col 65}{space 4} .0500198{col 78}{space 3} .1088026
{txt}{space 16}age3039 {c |}{col 25}{res}{space 2} .0069512{col 37}{space 2} .0225512{col 48}{space 1}    0.31{col 57}{space 3}0.761{col 65}{space 4}-.0395923{col 78}{space 3} .0534947
{txt}{space 16}age4049 {c |}{col 25}{res}{space 2}-.0557349{col 37}{space 2} .0264865{col 48}{space 1}   -2.10{col 57}{space 3}0.046{col 65}{space 4}-.1104002{col 78}{space 3}-.0010695
{txt}{space 16}age5059 {c |}{col 25}{res}{space 2} -.129241{col 37}{space 2} .0300187{col 48}{space 1}   -4.31{col 57}{space 3}0.000{col 65}{space 4}-.1911965{col 78}{space 3}-.0672855
{txt}{space 16}age6069 {c |}{col 25}{res}{space 2}-.0607454{col 37}{space 2} .0262081{col 48}{space 1}   -2.32{col 57}{space 3}0.029{col 65}{space 4}-.1148362{col 78}{space 3}-.0066547
{txt}{space 18}age70 {c |}{col 25}{res}{space 2} .0218758{col 37}{space 2} .0271804{col 48}{space 1}    0.80{col 57}{space 3}0.429{col 65}{space 4}-.0342217{col 78}{space 3} .0779733
{txt}{space 12}higher_educ {c |}{col 25}{res}{space 2}-.1025226{col 37}{space 2} .0167646{col 48}{space 1}   -6.12{col 57}{space 3}0.000{col 65}{space 4} -.137123{col 78}{space 3}-.0679222
{txt}{space 8}russian_speaker {c |}{col 25}{res}{space 2} .0520691{col 37}{space 2} .0245816{col 48}{space 1}    2.12{col 57}{space 3}0.045{col 65}{space 4} .0013351{col 78}{space 3} .1028031
{txt}{space 23} {c |}
{space 11}oblast_feb24 {c |}
{space 11}Kyiv oblast  {c |}{col 25}{res}{space 2}  .086469{col 37}{space 2}   .00453{col 48}{space 1}   19.09{col 57}{space 3}0.000{col 65}{space 4} .0771196{col 78}{space 3} .0958185
{txt}{space 6}Vinnytsya oblast  {c |}{col 25}{res}{space 2} .0024311{col 37}{space 2} .0082254{col 48}{space 1}    0.30{col 57}{space 3}0.770{col 65}{space 4}-.0145454{col 78}{space 3} .0194075
{txt}{space 10}Volyn oblast  {c |}{col 25}{res}{space 2} .0252407{col 37}{space 2} .0082734{col 48}{space 1}    3.05{col 57}{space 3}0.005{col 65}{space 4} .0081652{col 78}{space 3} .0423162
{txt}{space 1}Dnipropetrovsk oblast  {c |}{col 25}{res}{space 2} .1375472{col 37}{space 2} .0042507{col 48}{space 1}   32.36{col 57}{space 3}0.000{col 65}{space 4} .1287741{col 78}{space 3} .1463203
{txt}{space 8}Donetsk oblast  {c |}{col 25}{res}{space 2} .1653501{col 37}{space 2} .0103318{col 48}{space 1}   16.00{col 57}{space 3}0.000{col 65}{space 4} .1440264{col 78}{space 3} .1866738
{txt}{space 7}Zhytomyr oblast  {c |}{col 25}{res}{space 2} .1455896{col 37}{space 2} .0084042{col 48}{space 1}   17.32{col 57}{space 3}0.000{col 65}{space 4} .1282441{col 78}{space 3} .1629351
{txt}{space 4}Zakarpattya oblast  {c |}{col 25}{res}{space 2} .0450646{col 37}{space 2} .0070561{col 48}{space 1}    6.39{col 57}{space 3}0.000{col 65}{space 4} .0305016{col 78}{space 3} .0596277
{txt}{space 3}Zaporizhzhya oblast  {c |}{col 25}{res}{space 2} .0932218{col 37}{space 2} .0045553{col 48}{space 1}   20.46{col 57}{space 3}0.000{col 65}{space 4} .0838201{col 78}{space 3} .1026234
{txt}Ivano-Frankivsk oblast  {c |}{col 25}{res}{space 2} .0949479{col 37}{space 2} .0073615{col 48}{space 1}   12.90{col 57}{space 3}0.000{col 65}{space 4} .0797545{col 78}{space 3} .1101413
{txt}{space 5}Kirovohrad oblast  {c |}{col 25}{res}{space 2} .1741214{col 37}{space 2} .0107645{col 48}{space 1}   16.18{col 57}{space 3}0.000{col 65}{space 4} .1519046{col 78}{space 3} .1963383
{txt}{space 8}Luhansk oblast  {c |}{col 25}{res}{space 2} -.078088{col 37}{space 2} .0053569{col 48}{space 1}  -14.58{col 57}{space 3}0.000{col 65}{space 4}-.0891442{col 78}{space 3}-.0670318
{txt}{space 11}Lviv oblast  {c |}{col 25}{res}{space 2} .0596974{col 37}{space 2} .0065841{col 48}{space 1}    9.07{col 57}{space 3}0.000{col 65}{space 4} .0461085{col 78}{space 3} .0732862
{txt}{space 6}Mykolayiv oblast  {c |}{col 25}{res}{space 2} .1631052{col 37}{space 2} .0033517{col 48}{space 1}   48.66{col 57}{space 3}0.000{col 65}{space 4} .1561876{col 78}{space 3} .1700228
{txt}{space 10}Odesa oblast  {c |}{col 25}{res}{space 2} .0869546{col 37}{space 2} .0060443{col 48}{space 1}   14.39{col 57}{space 3}0.000{col 65}{space 4} .0744799{col 78}{space 3} .0994293
{txt}{space 8}Poltava oblast  {c |}{col 25}{res}{space 2} .0716223{col 37}{space 2} .0042102{col 48}{space 1}   17.01{col 57}{space 3}0.000{col 65}{space 4}  .062933{col 78}{space 3} .0803117
{txt}{space 10}Rivne oblast  {c |}{col 25}{res}{space 2}-.0249048{col 37}{space 2} .0072822{col 48}{space 1}   -3.42{col 57}{space 3}0.002{col 65}{space 4}-.0399346{col 78}{space 3} -.009875
{txt}{space 11}Sumy oblast  {c |}{col 25}{res}{space 2} .1131818{col 37}{space 2} .0058265{col 48}{space 1}   19.43{col 57}{space 3}0.000{col 65}{space 4} .1011565{col 78}{space 3} .1252072
{txt}{space 7}Ternopil oblast  {c |}{col 25}{res}{space 2}-.0605149{col 37}{space 2}   .00804{col 48}{space 1}   -7.53{col 57}{space 3}0.000{col 65}{space 4}-.0771086{col 78}{space 3}-.0439211
{txt}{space 8}Kharkiv oblast  {c |}{col 25}{res}{space 2} .0761785{col 37}{space 2} .0066685{col 48}{space 1}   11.42{col 57}{space 3}0.000{col 65}{space 4} .0624154{col 78}{space 3} .0899416
{txt}{space 8}Kherson oblast  {c |}{col 25}{res}{space 2} .0362967{col 37}{space 2} .0053867{col 48}{space 1}    6.74{col 57}{space 3}0.000{col 65}{space 4}  .025179{col 78}{space 3} .0474144
{txt}{space 3}Khmelnytskiy oblast  {c |}{col 25}{res}{space 2} .0326029{col 37}{space 2} .0055122{col 48}{space 1}    5.91{col 57}{space 3}0.000{col 65}{space 4} .0212263{col 78}{space 3} .0439795
{txt}{space 7}Cherkasy oblast  {c |}{col 25}{res}{space 2} .0057504{col 37}{space 2} .0089419{col 48}{space 1}    0.64{col 57}{space 3}0.526{col 65}{space 4}-.0127048{col 78}{space 3} .0242055
{txt}{space 5}Chernivtsi oblast  {c |}{col 25}{res}{space 2} .2135714{col 37}{space 2} .0064747{col 48}{space 1}   32.99{col 57}{space 3}0.000{col 65}{space 4} .2002084{col 78}{space 3} .2269345
{txt}{space 6}Chernihiv oblast  {c |}{col 25}{res}{space 2} -.017581{col 37}{space 2} .0067202{col 48}{space 1}   -2.62{col 57}{space 3}0.015{col 65}{space 4}-.0314509{col 78}{space 3}-.0037112
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .4912848{col 37}{space 2} .0174596{col 48}{space 1}   28.14{col 57}{space 3}0.000{col 65}{space 4}   .45525{col 78}{space 3} .5273196
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo m3: reg baseline_supp above_med_expos female age3039 age4049 age5059 age6069 age70 higher_educ russian_speaker i.oblast_feb24, vce(cluster oblast_feb24)

{txt}Linear regression                               Number of obs     = {res}     1,809
                                                {txt}{help j_robustsingular:F(8, 24) }         =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0659
                                                {txt}Root MSE          =    {res} .41325

{txt}{ralign 89:(Std. err. adjusted for {res:25} clusters in {res:oblast_feb24})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}       baseline_support{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}above_med_expos {c |}{col 25}{res}{space 2} .0273019{col 37}{space 2}  .031797{col 48}{space 1}    0.86{col 57}{space 3}0.399{col 65}{space 4}-.0383239{col 78}{space 3} .0929277
{txt}{space 17}female {c |}{col 25}{res}{space 2}  .082481{col 37}{space 2} .0136031{col 48}{space 1}    6.06{col 57}{space 3}0.000{col 65}{space 4} .0544055{col 78}{space 3} .1105565
{txt}{space 16}age3039 {c |}{col 25}{res}{space 2} .0023111{col 37}{space 2} .0214043{col 48}{space 1}    0.11{col 57}{space 3}0.915{col 65}{space 4}-.0418652{col 78}{space 3} .0464874
{txt}{space 16}age4049 {c |}{col 25}{res}{space 2}-.0560747{col 37}{space 2} .0246482{col 48}{space 1}   -2.28{col 57}{space 3}0.032{col 65}{space 4} -.106946{col 78}{space 3}-.0052034
{txt}{space 16}age5059 {c |}{col 25}{res}{space 2}-.1277717{col 37}{space 2} .0302452{col 48}{space 1}   -4.22{col 57}{space 3}0.000{col 65}{space 4}-.1901946{col 78}{space 3}-.0653487
{txt}{space 16}age6069 {c |}{col 25}{res}{space 2}-.0519466{col 37}{space 2} .0254917{col 48}{space 1}   -2.04{col 57}{space 3}0.053{col 65}{space 4}-.1045589{col 78}{space 3} .0006657
{txt}{space 18}age70 {c |}{col 25}{res}{space 2} .0354492{col 37}{space 2} .0322192{col 48}{space 1}    1.10{col 57}{space 3}0.282{col 65}{space 4}-.0310479{col 78}{space 3} .1019464
{txt}{space 12}higher_educ {c |}{col 25}{res}{space 2}-.1006447{col 37}{space 2} .0160341{col 48}{space 1}   -6.28{col 57}{space 3}0.000{col 65}{space 4}-.1337374{col 78}{space 3}-.0675519
{txt}{space 8}russian_speaker {c |}{col 25}{res}{space 2} .0628326{col 37}{space 2} .0233505{col 48}{space 1}    2.69{col 57}{space 3}0.013{col 65}{space 4} .0146395{col 78}{space 3} .1110257
{txt}{space 23} {c |}
{space 11}oblast_feb24 {c |}
{space 11}Kyiv oblast  {c |}{col 25}{res}{space 2} .0990562{col 37}{space 2} .0054782{col 48}{space 1}   18.08{col 57}{space 3}0.000{col 65}{space 4} .0877499{col 78}{space 3} .1103626
{txt}{space 6}Vinnytsya oblast  {c |}{col 25}{res}{space 2} .0252555{col 37}{space 2} .0111958{col 48}{space 1}    2.26{col 57}{space 3}0.033{col 65}{space 4} .0021485{col 78}{space 3} .0483625
{txt}{space 10}Volyn oblast  {c |}{col 25}{res}{space 2}  .040612{col 37}{space 2} .0158479{col 48}{space 1}    2.56{col 57}{space 3}0.017{col 65}{space 4} .0079037{col 78}{space 3} .0733204
{txt}{space 1}Dnipropetrovsk oblast  {c |}{col 25}{res}{space 2} .1501893{col 37}{space 2} .0050985{col 48}{space 1}   29.46{col 57}{space 3}0.000{col 65}{space 4} .1396666{col 78}{space 3} .1607121
{txt}{space 8}Donetsk oblast  {c |}{col 25}{res}{space 2} .1814303{col 37}{space 2} .0093796{col 48}{space 1}   19.34{col 57}{space 3}0.000{col 65}{space 4} .1620718{col 78}{space 3} .2007887
{txt}{space 7}Zhytomyr oblast  {c |}{col 25}{res}{space 2} .1680123{col 37}{space 2} .0102625{col 48}{space 1}   16.37{col 57}{space 3}0.000{col 65}{space 4} .1468316{col 78}{space 3}  .189193
{txt}{space 4}Zakarpattya oblast  {c |}{col 25}{res}{space 2} .1078711{col 37}{space 2} .0159641{col 48}{space 1}    6.76{col 57}{space 3}0.000{col 65}{space 4} .0749229{col 78}{space 3} .1408193
{txt}{space 3}Zaporizhzhya oblast  {c |}{col 25}{res}{space 2} .1188585{col 37}{space 2} .0048176{col 48}{space 1}   24.67{col 57}{space 3}0.000{col 65}{space 4} .1089155{col 78}{space 3} .1288014
{txt}Ivano-Frankivsk oblast  {c |}{col 25}{res}{space 2} .1037079{col 37}{space 2} .0167205{col 48}{space 1}    6.20{col 57}{space 3}0.000{col 65}{space 4} .0691984{col 78}{space 3} .1382174
{txt}{space 5}Kirovohrad oblast  {c |}{col 25}{res}{space 2}  .190011{col 37}{space 2} .0157297{col 48}{space 1}   12.08{col 57}{space 3}0.000{col 65}{space 4} .1575465{col 78}{space 3} .2224754
{txt}{space 8}Luhansk oblast  {c |}{col 25}{res}{space 2} -.053365{col 37}{space 2} .0096925{col 48}{space 1}   -5.51{col 57}{space 3}0.000{col 65}{space 4}-.0733694{col 78}{space 3}-.0333605
{txt}{space 11}Lviv oblast  {c |}{col 25}{res}{space 2} .0585683{col 37}{space 2} .0109527{col 48}{space 1}    5.35{col 57}{space 3}0.000{col 65}{space 4} .0359632{col 78}{space 3} .0811735
{txt}{space 6}Mykolayiv oblast  {c |}{col 25}{res}{space 2} .1747938{col 37}{space 2} .0055231{col 48}{space 1}   31.65{col 57}{space 3}0.000{col 65}{space 4} .1633947{col 78}{space 3} .1861929
{txt}{space 10}Odesa oblast  {c |}{col 25}{res}{space 2} .0838727{col 37}{space 2} .0089849{col 48}{space 1}    9.33{col 57}{space 3}0.000{col 65}{space 4} .0653287{col 78}{space 3} .1024167
{txt}{space 8}Poltava oblast  {c |}{col 25}{res}{space 2} .0829953{col 37}{space 2} .0154228{col 48}{space 1}    5.38{col 57}{space 3}0.000{col 65}{space 4} .0511643{col 78}{space 3} .1148264
{txt}{space 10}Rivne oblast  {c |}{col 25}{res}{space 2} .0069635{col 37}{space 2}  .010351{col 48}{space 1}    0.67{col 57}{space 3}0.508{col 65}{space 4}-.0143998{col 78}{space 3} .0283268
{txt}{space 11}Sumy oblast  {c |}{col 25}{res}{space 2} .1135311{col 37}{space 2} .0068034{col 48}{space 1}   16.69{col 57}{space 3}0.000{col 65}{space 4} .0994895{col 78}{space 3} .1275726
{txt}{space 7}Ternopil oblast  {c |}{col 25}{res}{space 2}-.0059645{col 37}{space 2} .0188109{col 48}{space 1}   -0.32{col 57}{space 3}0.754{col 65}{space 4}-.0447884{col 78}{space 3} .0328594
{txt}{space 8}Kharkiv oblast  {c |}{col 25}{res}{space 2} .0899694{col 37}{space 2} .0059799{col 48}{space 1}   15.05{col 57}{space 3}0.000{col 65}{space 4} .0776274{col 78}{space 3} .1023114
{txt}{space 8}Kherson oblast  {c |}{col 25}{res}{space 2} .0548889{col 37}{space 2} .0051604{col 48}{space 1}   10.64{col 57}{space 3}0.000{col 65}{space 4} .0442383{col 78}{space 3} .0655395
{txt}{space 3}Khmelnytskiy oblast  {c |}{col 25}{res}{space 2} .0458407{col 37}{space 2} .0119672{col 48}{space 1}    3.83{col 57}{space 3}0.001{col 65}{space 4} .0211416{col 78}{space 3} .0705398
{txt}{space 7}Cherkasy oblast  {c |}{col 25}{res}{space 2} .0422792{col 37}{space 2} .0129044{col 48}{space 1}    3.28{col 57}{space 3}0.003{col 65}{space 4} .0156458{col 78}{space 3} .0689126
{txt}{space 5}Chernivtsi oblast  {c |}{col 25}{res}{space 2} .2544346{col 37}{space 2} .0145715{col 48}{space 1}   17.46{col 57}{space 3}0.000{col 65}{space 4} .2243605{col 78}{space 3} .2845088
{txt}{space 6}Chernihiv oblast  {c |}{col 25}{res}{space 2}-.0075922{col 37}{space 2} .0087095{col 48}{space 1}   -0.87{col 57}{space 3}0.392{col 65}{space 4}-.0255676{col 78}{space 3} .0103833
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .4326433{col 37}{space 2} .0231389{col 48}{space 1}   18.70{col 57}{space 3}0.000{col 65}{space 4} .3848869{col 78}{space 3} .4803997
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. coefplot (m1, msymbol(o) label(Injured family and friends)) (m2, msymbol(d) label(Killed family and friends)) (m3, msymbol(t) label(Heard gunfire)), ylabel(none) xline(0) title("Exposure to Violence and" "Baseline Support for Agreement" ) levels (95 90) keep (above_med_expos above_med_exposure1 above_med_exposure2)
{res}{txt}
{com}. 
. quietly graph export figure9.jpg, replace
{txt}
{com}. 
. eststo clear
{txt}
{com}. 
. restore
{txt}
{com}. 
. 
. 
. ***FIGURE 10: Military optimism and support for an agreement
. 
. 
. *Military optimism variables (success due to skill of Ukraine's soldiers minus success due to the weakness of Russian army)
. gen miloptimism = milsuccess_q9_norm-milsuccess_q13_norm
{txt}(2,805 missing values generated)

{com}. la var miloptimism "Military optimism"
{txt}
{com}. sum miloptimism, de

                      {txt}Military optimism
{hline 61}
      Percentiles      Smallest
 1%    {res}      -.5             -1
{txt} 5%    {res}      -.1             -1
{txt}10%    {res}        0             -1       {txt}Obs         {res}      5,682
{txt}25%    {res}        0             -1       {txt}Sum of wgt. {res}      5,682

{txt}50%    {res}       .3                      {txt}Mean          {res} .2997536
                        {txt}Largest       Std. dev.     {res} .3129713
{txt}75%    {res}       .5              1
{txt}90%    {res}       .7              1       {txt}Variance      {res}  .097951
{txt}95%    {res}        1              1       {txt}Skewness      {res} .1799312
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.220997
{txt}
{com}. 
. gen above_median_miloptimism=.
{txt}(8,487 missing values generated)

{com}. replace above_median_miloptimism=0 if miloptimism<=0.3 & miloptimism!=.
{txt}(2,643 real changes made)

{com}. replace above_median_miloptimism=1 if miloptimism>0.3 & miloptimism!=.
{txt}(3,039 real changes made)

{com}. sum above_median_miloptimism

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
above_medi~m {c |}{res}      5,682    .5348469    .4988281          0          1
{txt}
{com}. 
. 
. eststo m1: reg baseline_support above_median_miloptimism pca_violexposure0_1, vce(robust)

{txt}Linear regression                               Number of obs     = {res}     3,536
                                                {txt}F(2, 3533)        =  {res}     9.99
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0055
                                                {txt}Root MSE          =    {res} .39935

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}        baseline_support{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
above_median_miloptimism {c |}{col 26}{res}{space 2}-.0451787{col 38}{space 2} .0134696{col 49}{space 1}   -3.35{col 58}{space 3}0.001{col 66}{space 4}-.0715877{col 79}{space 3}-.0187696
{txt}{space 5}pca_violexposure0_1 {c |}{col 26}{res}{space 2} .1010629{col 38}{space 2} .0358349{col 49}{space 1}    2.82{col 58}{space 3}0.005{col 66}{space 4} .0308037{col 79}{space 3} .1713221
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} .5959413{col 38}{space 2} .0135092{col 49}{space 1}   44.11{col 58}{space 3}0.000{col 66}{space 4} .5694547{col 79}{space 3} .6224279
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo m2: reghdfe baseline_support above_median_miloptimism pca_violexposure0_1 age, a(oblast_feb24 i.q2 i.q3 i.q4 i.q6 q7_1 q8_1 q8_2 q8_3 q8_4 q8_5 q8_7) vce(cluster oblast_feb24)
{res}{txt}(dropped 2 {browse "http://scorreia.com/research/singletons.pdf":singleton observations})
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 8 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     3,534
{txt}Absorbing 12 HDFE groups{col 51}F({res}   3{txt},{res}     24{txt}){col 67}= {res}     16.90
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0000
{txt}{col 51}R-squared{col 67}= {res}    0.0827
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0676
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0167
{txt}{col 1}Number of clusters ({res}oblast_feb24{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.3866

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_feb24})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}        baseline_support{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
above_median_miloptimism {c |}{col 26}{res}{space 2}-.0397072{col 38}{space 2}  .016437{col 49}{space 1}   -2.42{col 58}{space 3}0.024{col 66}{space 4}-.0736314{col 79}{space 3} -.005783
{txt}{space 5}pca_violexposure0_1 {c |}{col 26}{res}{space 2} .0542913{col 38}{space 2} .0447208{col 49}{space 1}    1.21{col 58}{space 3}0.237{col 66}{space 4}-.0380079{col 79}{space 3} .1465906
{txt}{space 21}age {c |}{col 26}{res}{space 2}-.0031402{col 38}{space 2} .0005601{col 49}{space 1}   -5.61{col 58}{space 3}0.000{col 66}{space 4}-.0042962{col 79}{space 3}-.0019841
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} .7420876{col 38}{space 2} .0274102{col 49}{space 1}   27.07{col 58}{space 3}0.000{col 66}{space 4} .6855158{col 79}{space 3} .7986594
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 14}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}  Absorbed FE{col 15}{c |} Categories{col 28} - Redundant{col 40}  = Num. Coefs{col 55}{c |}
{res}{col 1}{text}{hline 14}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} oblast_feb24{col 15}{c |}{space 1}       25{col 28}{space 1}       25{col 40}{result}{space 1}        0{col 54}{text}*{col 55}{c |}
{res}{col 1}{text}           q2{col 15}{c |}{space 1}        2{col 28}{space 1}        1{col 40}{result}{space 1}        1{col 54}{text} {col 55}{c |}
{res}{col 1}{text}           q3{col 15}{c |}{space 1}        7{col 28}{space 1}        1{col 40}{result}{space 1}        6{col 54}{text} {col 55}{c |}
{res}{col 1}{text}           q4{col 15}{c |}{space 1}        6{col 28}{space 1}        1{col 40}{result}{space 1}        5{col 54}{text}?{col 55}{c |}
{res}{col 1}{text}           q6{col 15}{c |}{space 1}       12{col 28}{space 1}        1{col 40}{result}{space 1}       11{col 54}{text}?{col 55}{c |}
{res}{col 1}{text}         q7_1{col 15}{c |}{space 1}        2{col 28}{space 1}        1{col 40}{result}{space 1}        1{col 54}{text}?{col 55}{c |}
{res}{col 1}{text}         q8_1{col 15}{c |}{space 1}        2{col 28}{space 1}        1{col 40}{result}{space 1}        1{col 54}{text}?{col 55}{c |}
{res}{col 1}{text}         q8_2{col 15}{c |}{space 1}        2{col 28}{space 1}        1{col 40}{result}{space 1}        1{col 54}{text}?{col 55}{c |}
{res}{col 1}{text}         q8_3{col 15}{c |}{space 1}        2{col 28}{space 1}        1{col 40}{result}{space 1}        1{col 54}{text}?{col 55}{c |}
{res}{col 1}{text}         q8_4{col 15}{c |}{space 1}        2{col 28}{space 1}        1{col 40}{result}{space 1}        1{col 54}{text}?{col 55}{c |}
{res}{col 1}{text}         q8_5{col 15}{c |}{space 1}        2{col 28}{space 1}        1{col 40}{result}{space 1}        1{col 54}{text}?{col 55}{c |}
{res}{col 1}{text}         q8_7{col 15}{c |}{space 1}        2{col 28}{space 1}        1{col 40}{result}{space 1}        1{col 54}{text}?{col 55}{c |}
{res}{col 1}{text}{hline 14}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}
{com}. 
. coefplot (m1, label(Baseline)) (m2, label(Saturated)), vertical keep(above_median_miloptimism) xlabel( ,angle(45))  levels(95 90) title("Support for settlement with Russia") xlabel(1 "Military optimism")
{res}{txt}
{com}. 
. quietly graph export figure10.jpg, replace
{txt}
{com}. 
. eststo clear
{txt}
{com}. 
. 
. ********************************************************************************
. *Replicating appendix figures
. ********************************************************************************
. 
. 
. ***TABLE A-1: Descriptive statistics
. 
. estpost sum age1829 age3039 age4049 age5059 age6069 age70 female higher_educ russian_speaker oblast_feb24_* if wave2==1

{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:age1829}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .2307692}}}{space 1}{space 1}{ralign 9:{res:{sf: .1776175}}}{space 1}{space 1}{ralign 9:{res:{sf: .4214469}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      399}}}{space 1}
{space 0}{space 0}{ralign 12:age3039}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .3342973}}}{space 1}{space 1}{ralign 9:{res:{sf: .2226714}}}{space 1}{space 1}{ralign 9:{res:{sf: .4718807}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      578}}}{space 1}
{space 0}{space 0}{ralign 12:age4049}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .3204164}}}{space 1}{space 1}{ralign 9:{res:{sf: .2178758}}}{space 1}{space 1}{ralign 9:{res:{sf: .4667716}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      554}}}{space 1}
{space 0}{space 0}{ralign 12:age5059}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .1145171}}}{space 1}{space 1}{ralign 9:{res:{sf: .1014616}}}{space 1}{space 1}{ralign 9:{res:{sf: .3185304}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      198}}}{space 1}
{space 0}{space 0}{ralign 12:age6069}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:age70}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:female}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .5234239}}}{space 1}{space 1}{ralign 9:{res:{sf: .2495957}}}{space 1}{space 1}{ralign 9:{res:{sf: .4995955}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      905}}}{space 1}
{space 0}{space 0}{ralign 12:higher_educ}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1726}}}{space 1}{space 1}{ralign 9:{res:{sf:     1726}}}{space 1}{space 1}{ralign 9:{res:{sf: .6865585}}}{space 1}{space 1}{ralign 9:{res:{sf: .2153207}}}{space 1}{space 1}{ralign 9:{res:{sf: .4640266}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:     1185}}}{space 1}
{space 0}{space 0}{ralign 12:russian_sp~r}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1719}}}{space 1}{space 1}{ralign 9:{res:{sf:     1719}}}{space 1}{space 1}{ralign 9:{res:{sf: .3752182}}}{space 1}{space 1}{ralign 9:{res:{sf: .2345659}}}{space 1}{space 1}{ralign 9:{res:{sf: .4843201}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      645}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .1769809}}}{space 1}{space 1}{ralign 9:{res:{sf:  .145743}}}{space 1}{space 1}{ralign 9:{res:{sf:  .381763}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      306}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .0179294}}}{space 1}{space 1}{ralign 9:{res:{sf: .0176182}}}{space 1}{space 1}{ralign 9:{res:{sf: .1327334}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       31}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .0364372}}}{space 1}{space 1}{ralign 9:{res:{sf: .0351299}}}{space 1}{space 1}{ralign 9:{res:{sf: .1874297}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       63}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .0133025}}}{space 1}{space 1}{ralign 9:{res:{sf: .0131331}}}{space 1}{space 1}{ralign 9:{res:{sf: .1145999}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       23}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_5}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .1104685}}}{space 1}{space 1}{ralign 9:{res:{sf: .0983221}}}{space 1}{space 1}{ralign 9:{res:{sf: .3135635}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      191}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_6}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .0462695}}}{space 1}{space 1}{ralign 9:{res:{sf: .0441542}}}{space 1}{space 1}{ralign 9:{res:{sf:  .210129}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       80}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_7}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .0289184}}}{space 1}{space 1}{ralign 9:{res:{sf: .0280984}}}{space 1}{space 1}{ralign 9:{res:{sf: .1676258}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       50}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_8}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .0092539}}}{space 1}{space 1}{ralign 9:{res:{sf: .0091736}}}{space 1}{space 1}{ralign 9:{res:{sf: .0957788}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       16}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_9}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .0636206}}}{space 1}{space 1}{ralign 9:{res:{sf: .0596075}}}{space 1}{space 1}{ralign 9:{res:{sf: .2441464}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      110}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~10}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .0150376}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01482}}}{space 1}{space 1}{ralign 9:{res:{sf: .1217376}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       26}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~11}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .0138809}}}{space 1}{space 1}{ralign 9:{res:{sf: .0136961}}}{space 1}{space 1}{ralign 9:{res:{sf: .1170303}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       24}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~12}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf:     1729}}}{space 1}{space 1}{ralign 9:{res:{sf: .0121457}}}{space 1}{space 1}{ralign 9:{res:{sf: .0120052}}}{space 1}{space 1}{ralign 9:{res:{sf: .1095681}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       21}}}{space 1}
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{com}. eststo m1
{txt}
{com}. 
. estpost sum age1829 age3039 age4049 age5059 age6069 age70 female higher_educ russian_speaker oblast_feb24_* if wave2==2

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{com}. eststo m3
{txt}
{com}. 
. estpost sum age1829 age3039 age4049 age5059 age6069 age70 female higher_educ russian_speaker oblast_feb24_* if wave2==4

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{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
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{space 0}{space 0}{ralign 12:age3039}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .1917536}}}{space 1}{space 1}{ralign 9:{res:{sf: .1550612}}}{space 1}{space 1}{ralign 9:{res:{sf: .3937781}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      386}}}{space 1}
{space 0}{space 0}{ralign 12:age4049}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .1902633}}}{space 1}{space 1}{ralign 9:{res:{sf: .1541397}}}{space 1}{space 1}{ralign 9:{res:{sf: .3926063}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      383}}}{space 1}
{space 0}{space 0}{ralign 12:age5059}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .1783408}}}{space 1}{space 1}{ralign 9:{res:{sf: .1466082}}}{space 1}{space 1}{ralign 9:{res:{sf: .3828945}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      359}}}{space 1}
{space 0}{space 0}{ralign 12:age6069}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .1897665}}}{space 1}{space 1}{ralign 9:{res:{sf: .1538316}}}{space 1}{space 1}{ralign 9:{res:{sf: .3922137}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      382}}}{space 1}
{space 0}{space 0}{ralign 12:age70}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .1162444}}}{space 1}{space 1}{ralign 9:{res:{sf: .1027827}}}{space 1}{space 1}{ralign 9:{res:{sf: .3205974}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      234}}}{space 1}
{space 0}{space 0}{ralign 12:female}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:  .561848}}}{space 1}{space 1}{ralign 9:{res:{sf: .2462972}}}{space 1}{space 1}{ralign 9:{res:{sf: .4962834}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:     1131}}}{space 1}
{space 0}{space 0}{ralign 12:higher_educ}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2008}}}{space 1}{space 1}{ralign 9:{res:{sf:     2008}}}{space 1}{space 1}{ralign 9:{res:{sf: .4337649}}}{space 1}{space 1}{ralign 9:{res:{sf: .2457353}}}{space 1}{space 1}{ralign 9:{res:{sf:  .495717}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      871}}}{space 1}
{space 0}{space 0}{ralign 12:russian_sp~r}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2010}}}{space 1}{space 1}{ralign 9:{res:{sf:     2010}}}{space 1}{space 1}{ralign 9:{res:{sf: .1850746}}}{space 1}{space 1}{ralign 9:{res:{sf: .1508971}}}{space 1}{space 1}{ralign 9:{res:{sf: .3884547}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      372}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:  .099851}}}{space 1}{space 1}{ralign 9:{res:{sf: .0899254}}}{space 1}{space 1}{ralign 9:{res:{sf: .2998757}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      201}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:  .065077}}}{space 1}{space 1}{ralign 9:{res:{sf: .0608722}}}{space 1}{space 1}{ralign 9:{res:{sf:  .246723}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      131}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0352707}}}{space 1}{space 1}{ralign 9:{res:{sf: .0340436}}}{space 1}{space 1}{ralign 9:{res:{sf: .1845092}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       71}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0188773}}}{space 1}{space 1}{ralign 9:{res:{sf: .0185302}}}{space 1}{space 1}{ralign 9:{res:{sf: .1361255}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       38}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_5}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0963736}}}{space 1}{space 1}{ralign 9:{res:{sf:  .087129}}}{space 1}{space 1}{ralign 9:{res:{sf: .2951762}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      194}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_6}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0501739}}}{space 1}{space 1}{ralign 9:{res:{sf: .0476801}}}{space 1}{space 1}{ralign 9:{res:{sf: .2183578}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      101}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_7}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0293095}}}{space 1}{space 1}{ralign 9:{res:{sf: .0284646}}}{space 1}{space 1}{ralign 9:{res:{sf: .1687145}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       59}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_8}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0208644}}}{space 1}{space 1}{ralign 9:{res:{sf: .0204392}}}{space 1}{space 1}{ralign 9:{res:{sf: .1429658}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       42}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~_9}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0377546}}}{space 1}{space 1}{ralign 9:{res:{sf: .0363472}}}{space 1}{space 1}{ralign 9:{res:{sf: .1906495}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       76}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~10}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0248385}}}{space 1}{space 1}{ralign 9:{res:{sf: .0242336}}}{space 1}{space 1}{ralign 9:{res:{sf: .1556716}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       50}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~11}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0233482}}}{space 1}{space 1}{ralign 9:{res:{sf: .0228144}}}{space 1}{space 1}{ralign 9:{res:{sf: .1510445}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       47}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~12}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0099354}}}{space 1}{space 1}{ralign 9:{res:{sf: .0098416}}}{space 1}{space 1}{ralign 9:{res:{sf: .0992048}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       20}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~13}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0457029}}}{space 1}{space 1}{ralign 9:{res:{sf: .0436359}}}{space 1}{space 1}{ralign 9:{res:{sf:  .208892}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       92}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~14}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0382514}}}{space 1}{space 1}{ralign 9:{res:{sf: .0368065}}}{space 1}{space 1}{ralign 9:{res:{sf: .1918502}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       77}}}{space 1}
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{space 0}{space 0}{ralign 12:oblast_fe~17}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0183805}}}{space 1}{space 1}{ralign 9:{res:{sf: .0180517}}}{space 1}{space 1}{ralign 9:{res:{sf: .1343564}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       37}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~18}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0298063}}}{space 1}{space 1}{ralign 9:{res:{sf: .0289322}}}{space 1}{space 1}{ralign 9:{res:{sf: .1700947}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       60}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~19}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0183805}}}{space 1}{space 1}{ralign 9:{res:{sf: .0180517}}}{space 1}{space 1}{ralign 9:{res:{sf: .1343564}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       37}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~20}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0909091}}}{space 1}{space 1}{ralign 9:{res:{sf: .0826857}}}{space 1}{space 1}{ralign 9:{res:{sf: .2875512}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      183}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~21}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0233482}}}{space 1}{space 1}{ralign 9:{res:{sf: .0228144}}}{space 1}{space 1}{ralign 9:{res:{sf: .1510445}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       47}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~22}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0293095}}}{space 1}{space 1}{ralign 9:{res:{sf: .0284646}}}{space 1}{space 1}{ralign 9:{res:{sf: .1687145}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       59}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~23}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0317933}}}{space 1}{space 1}{ralign 9:{res:{sf: .0307978}}}{space 1}{space 1}{ralign 9:{res:{sf: .1754931}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       64}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_f~_24}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0158967}}}{space 1}{space 1}{ralign 9:{res:{sf: .0156517}}}{space 1}{space 1}{ralign 9:{res:{sf: .1251069}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       32}}}{space 1}
{space 0}{space 0}{ralign 12:oblast_fe~25}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf:     2013}}}{space 1}{space 1}{ralign 9:{res:{sf: .0407352}}}{space 1}{space 1}{ralign 9:{res:{sf: .0390953}}}{space 1}{space 1}{ralign 9:{res:{sf: .1977253}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       82}}}{space 1}

{com}. eststo m4
{txt}
{com}. 
. esttab m1 m3 m4 using table_a1.tex, replace mtitles("\textbf{c -(}\emph{c -(}Online{c )-}{c )-}" "\textbf{c -(}\emph{c -(}Telephone wave 1{c )-}{c )-}" "\textbf{c -(}\emph{c -(}Telephone wave 2{c )-}{c )-}") ///
> collabels(\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}{c -(}Mean{c )-}{c )-} \multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}{c -(}Std.Dev.{c )-}{c )-} \multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}{c -(}N{c )-}{c )-}) /// 
> cells("mean(fmt(2)) sd(fmt(2)) count(fmt(0))") label nonumber f noobs alignment(S) booktabs
{res}{txt}(output written to {browse  `"table_a1.tex"'})

{com}. 
. eststo clear
{txt}
{com}. 
. 
. ***FIGURE A-4
. *A-4a: Principles of negotiation
. 
. 
. matrix survey_matrix = J(24,4,.)        
{txt}
{com}.                 
. summarize q13_norm if wave==1 & online==1, detail

           {txt}Ukraine should not negotiate under any
                        circumstances
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}      .25              0       {txt}Obs         {res}      1,515
{txt}25%    {res}      .25              0       {txt}Sum of wgt. {res}      1,515

{txt}50%    {res}      .75                      {txt}Mean          {res} .6330033
                        {txt}Largest       Std. dev.     {res} .3406613
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1160501
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.4347708
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.866348
{txt}
{com}. matrix survey_matrix[1,1] = 1
{txt}
{com}. matrix survey_matrix[1,2] = r(mean)
{txt}
{com}. matrix survey_matrix[1,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[1,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q13_norm if wave==2 & online==1, detail

           {txt}Ukraine should not negotiate under any
                        circumstances
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}      .25              0       {txt}Obs         {res}      1,560
{txt}25%    {res}      .25              0       {txt}Sum of wgt. {res}      1,560

{txt}50%    {res}      .75                      {txt}Mean          {res} .6415064
                        {txt}Largest       Std. dev.     {res} .3378515
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1141437
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.5034886
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.981039
{txt}
{com}. matrix survey_matrix[2,1] = 1.25
{txt}
{com}. matrix survey_matrix[2,2] = r(mean)
{txt}
{com}. matrix survey_matrix[2,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[2,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q13_norm if wave==1 & online==0, detail

           {txt}Ukraine should not negotiate under any
                        circumstances
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,834
{txt}25%    {res}      .25              0       {txt}Sum of wgt. {res}      2,834

{txt}50%    {res}      .75                      {txt}Mean          {res} .5954481
                        {txt}Largest       Std. dev.     {res} .3812038
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1453164
{txt}95%    {res}        1              1       {txt}Skewness      {res} -.294184
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.538811
{txt}
{com}. matrix survey_matrix[3,1] = 1.5
{txt}
{com}. matrix survey_matrix[3,2] = r(mean)
{txt}
{com}. matrix survey_matrix[3,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[3,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q14_norm if wave==1 & online==1, detail

        {txt}Morally wrong to sell out dead w/ peace deal
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}      .25              0       {txt}Obs         {res}      1,557
{txt}25%    {res}      .75              0       {txt}Sum of wgt. {res}      1,557

{txt}50%    {res}        1                      {txt}Mean          {res} .7668593
                        {txt}Largest       Std. dev.     {res} .3299236
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1088496
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.280441
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.318077
{txt}
{com}. matrix survey_matrix[4,1] = 2
{txt}
{com}. matrix survey_matrix[4,2] = r(mean)
{txt}
{com}. matrix survey_matrix[4,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[4,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q14_norm if wave==2 & online==1, detail

        {txt}Morally wrong to sell out dead w/ peace deal
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}      .25              0       {txt}Obs         {res}      1,577
{txt}25%    {res}      .75              0       {txt}Sum of wgt. {res}      1,577

{txt}50%    {res}        1                      {txt}Mean          {res} .7813887
                        {txt}Largest       Std. dev.     {res}   .30626
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0937952
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.344389
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.702539
{txt}
{com}. matrix survey_matrix[5,1] = 2.25
{txt}
{com}. matrix survey_matrix[5,2] = r(mean)
{txt}
{com}. matrix survey_matrix[5,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[5,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q14_norm if wave==1 & online==0, detail

        {txt}Morally wrong to sell out dead w/ peace deal
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,816
{txt}25%    {res}       .5              0       {txt}Sum of wgt. {res}      2,816

{txt}50%    {res}        1                      {txt}Mean          {res} .7490234
                        {txt}Largest       Std. dev.     {res} .3629413
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1317264
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.160913
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.785301
{txt}
{com}. matrix survey_matrix[6,1] = 2.5
{txt}
{com}. matrix survey_matrix[6,2] = r(mean)
{txt}
{com}. matrix survey_matrix[6,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[6,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q15_norm if wave==1 & online==1, detail

                  {txt}Russian cannot be trusted
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}      .25              0
{txt}10%    {res}       .5              0       {txt}Obs         {res}      1,604
{txt}25%    {res}      .75              0       {txt}Sum of wgt. {res}      1,604

{txt}50%    {res}        1                      {txt}Mean          {res} .8645574
                        {txt}Largest       Std. dev.     {res}  .238605
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0569324
{txt}95%    {res}        1              1       {txt}Skewness      {res}-2.100653
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 7.109763
{txt}
{com}. matrix survey_matrix[7,1] = 3
{txt}
{com}. matrix survey_matrix[7,2] = r(mean)
{txt}
{com}. matrix survey_matrix[7,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[7,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q15_norm if wave==2 & online==1, detail

                  {txt}Russian cannot be trusted
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}      .25              0
{txt}10%    {res}       .5              0       {txt}Obs         {res}      1,622
{txt}25%    {res}      .75              0       {txt}Sum of wgt. {res}      1,622

{txt}50%    {res}        1                      {txt}Mean          {res} .8697596
                        {txt}Largest       Std. dev.     {res}  .234546
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0550118
{txt}95%    {res}        1              1       {txt}Skewness      {res}-2.107167
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 7.107326
{txt}
{com}. matrix survey_matrix[8,1] = 3.25
{txt}
{com}. matrix survey_matrix[8,2] = r(mean)
{txt}
{com}. matrix survey_matrix[8,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[8,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q15_norm if wave==1 & online==0, detail

                  {txt}Russian cannot be trusted
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}      .25              0       {txt}Obs         {res}      2,902
{txt}25%    {res}      .75              0       {txt}Sum of wgt. {res}      2,902

{txt}50%    {res}        1                      {txt}Mean          {res} .8265851
                        {txt}Largest       Std. dev.     {res} .3087495
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0953263
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.747739
{txt}99%    {res}        1              1       {txt}Kurtosis      {res}  4.73637
{txt}
{com}. matrix survey_matrix[9,1] = 3.5
{txt}
{com}. matrix survey_matrix[9,2] = r(mean)
{txt}
{com}. matrix survey_matrix[9,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[9,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q16_norm if wave==1 & online==1, detail

                  {txt}Russia will exploit peace
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}      .25              0
{txt}10%    {res}       .5              0       {txt}Obs         {res}      1,523
{txt}25%    {res}      .75              0       {txt}Sum of wgt. {res}      1,523

{txt}50%    {res}        1                      {txt}Mean          {res} .8553841
                        {txt}Largest       Std. dev.     {res} .2336448
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0545899
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.952675
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 6.701225
{txt}
{com}. matrix survey_matrix[10,1] = 4
{txt}
{com}. matrix survey_matrix[10,2] = r(mean)
{txt}
{com}. matrix survey_matrix[10,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[10,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q16_norm if wave==2 & online==1, detail

                  {txt}Russia will exploit peace
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}      .25              0
{txt}10%    {res}       .5              0       {txt}Obs         {res}      1,575
{txt}25%    {res}      .75              0       {txt}Sum of wgt. {res}      1,575

{txt}50%    {res}        1                      {txt}Mean          {res} .8498413
                        {txt}Largest       Std. dev.     {res} .2400993
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0576477
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.884488
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 6.245313
{txt}
{com}. matrix survey_matrix[11,1] = 4.25
{txt}
{com}. matrix survey_matrix[11,2] = r(mean)
{txt}
{com}. matrix survey_matrix[11,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[11,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q16_norm if wave==1 & online==0, detail

                  {txt}Russia will exploit peace
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}      .25              0       {txt}Obs         {res}      2,837
{txt}25%    {res}      .75              0       {txt}Sum of wgt. {res}      2,837

{txt}50%    {res}        1                      {txt}Mean          {res} .8118611
                        {txt}Largest       Std. dev.     {res} .3121134
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0974148
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.668186
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 4.517609
{txt}
{com}. matrix survey_matrix[12,1] = 4.5
{txt}
{com}. matrix survey_matrix[12,2] = r(mean)
{txt}
{com}. matrix survey_matrix[12,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[12,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q17_norm if wave==1 & online==1, detail

                {txt}Peace is morally right thing
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,494
{txt}25%    {res}      .25              0       {txt}Sum of wgt. {res}      1,494

{txt}50%    {res}       .5                      {txt}Mean          {res} .4764056
                        {txt}Largest       Std. dev.     {res} .3441753
{txt}75%    {res}      .75              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1184567
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.0027132
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.674707
{txt}
{com}. matrix survey_matrix[13,1] = 5
{txt}
{com}. matrix survey_matrix[13,2] = r(mean)
{txt}
{com}. matrix survey_matrix[13,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[13,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q17_norm if wave==2 & online==1, detail

                {txt}Peace is morally right thing
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,542
{txt}25%    {res}      .25              0       {txt}Sum of wgt. {res}      1,542

{txt}50%    {res}       .5                      {txt}Mean          {res} .4682231
                        {txt}Largest       Std. dev.     {res} .3439643
{txt}75%    {res}      .75              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1183114
{txt}95%    {res}        1              1       {txt}Skewness      {res} .0452418
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.679307
{txt}
{com}. matrix survey_matrix[14,1] = 5.25
{txt}
{com}. matrix survey_matrix[14,2] = r(mean)
{txt}
{com}. matrix survey_matrix[14,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[14,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q17_norm if wave==1 & online==0, detail

                {txt}Peace is morally right thing
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,663
{txt}25%    {res}      .25              0       {txt}Sum of wgt. {res}      2,663

{txt}50%    {res}       .5                      {txt}Mean          {res} .5104206
                        {txt}Largest       Std. dev.     {res} .3759652
{txt}75%    {res}      .75              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1413499
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.1179861
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.534714
{txt}
{com}. matrix survey_matrix[15,1] = 5.5
{txt}
{com}. matrix survey_matrix[15,2] = r(mean)
{txt}
{com}. matrix survey_matrix[15,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[15,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q18_norm if wave==1 & online==1, detail

       {txt}Ukraine must do whatever it takes to make peace
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}      .25              0       {txt}Obs         {res}      1,560
{txt}25%    {res}       .5              0       {txt}Sum of wgt. {res}      1,560

{txt}50%    {res}      .75                      {txt}Mean          {res} .7355769
                        {txt}Largest       Std. dev.     {res}  .308424
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0951254
{txt}95%    {res}        1              1       {txt}Skewness      {res}  -1.1041
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.178891
{txt}
{com}. matrix survey_matrix[16,1] = 6
{txt}
{com}. matrix survey_matrix[16,2] = r(mean)
{txt}
{com}. matrix survey_matrix[16,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[16,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q18_norm if wave==2 & online==1, detail

       {txt}Ukraine must do whatever it takes to make peace
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}      .25              0       {txt}Obs         {res}      1,570
{txt}25%    {res}       .5              0       {txt}Sum of wgt. {res}      1,570

{txt}50%    {res}      .75                      {txt}Mean          {res} .7090764
                        {txt}Largest       Std. dev.     {res}  .302484
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0914966
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.8847752
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.785069
{txt}
{com}. matrix survey_matrix[17,1] = 6.25
{txt}
{com}. matrix survey_matrix[17,2] = r(mean)
{txt}
{com}. matrix survey_matrix[17,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[17,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q18_norm if wave==1 & online==0, detail

       {txt}Ukraine must do whatever it takes to make peace
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}      .25              0       {txt}Obs         {res}      2,800
{txt}25%    {res}      .75              0       {txt}Sum of wgt. {res}      2,800

{txt}50%    {res}        1                      {txt}Mean          {res} .7966071
                        {txt}Largest       Std. dev.     {res} .3060471
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0936648
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.480702
{txt}99%    {res}        1              1       {txt}Kurtosis      {res}  4.03098
{txt}
{com}. matrix survey_matrix[18,1] = 6.5
{txt}
{com}. matrix survey_matrix[18,2] = r(mean)
{txt}
{com}. matrix survey_matrix[18,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[18,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q19_norm if wave==1 & online==1, detail

          {txt}Ukraine must make territorial concessions
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,548
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,548

{txt}50%    {res}        0                      {txt}Mean          {res} .1870155
                        {txt}Largest       Std. dev.     {res}  .286785
{txt}75%    {res}      .25              1
{txt}90%    {res}      .75              1       {txt}Variance      {res} .0822456
{txt}95%    {res}      .75              1       {txt}Skewness      {res} 1.466419
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 4.030535
{txt}
{com}. matrix survey_matrix[19,1] = 7
{txt}
{com}. matrix survey_matrix[19,2] = r(mean)
{txt}
{com}. matrix survey_matrix[19,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[19,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q19_norm if wave==2 & online==1, detail

          {txt}Ukraine must make territorial concessions
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,591
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,591

{txt}50%    {res}        0                      {txt}Mean          {res} .1910748
                        {txt}Largest       Std. dev.     {res} .2942459
{txt}75%    {res}      .25              1
{txt}90%    {res}      .75              1       {txt}Variance      {res} .0865807
{txt}95%    {res}      .75              1       {txt}Skewness      {res} 1.462136
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.974782
{txt}
{com}. matrix survey_matrix[20,1] = 7.25
{txt}
{com}. matrix survey_matrix[20,2] = r(mean)
{txt}
{com}. matrix survey_matrix[20,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[20,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q19_norm if wave==1 & online==0, detail

          {txt}Ukraine must make territorial concessions
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,836
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      2,836

{txt}50%    {res}        0                      {txt}Mean          {res}  .127909
                        {txt}Largest       Std. dev.     {res} .2478575
{txt}75%    {res}      .25              1
{txt}90%    {res}       .5              1       {txt}Variance      {res} .0614333
{txt}95%    {res}      .75              1       {txt}Skewness      {res} 2.101901
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 6.630804
{txt}
{com}. matrix survey_matrix[21,1] = 7.5
{txt}
{com}. matrix survey_matrix[21,2] = r(mean)
{txt}
{com}. matrix survey_matrix[21,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[21,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q20_norm if wave==1 & online==1, detail

                {txt}Strategic to keep negotiating
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,445
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,445

{txt}50%    {res}       .5                      {txt}Mean          {res} .4252595
                        {txt}Largest       Std. dev.     {res} .3350001
{txt}75%    {res}      .75              1
{txt}90%    {res}      .75              1       {txt}Variance      {res} .1122251
{txt}95%    {res}        1              1       {txt}Skewness      {res} .1226818
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.700922
{txt}
{com}. matrix survey_matrix[22,1] = 8
{txt}
{com}. matrix survey_matrix[22,2] = r(mean)
{txt}
{com}. matrix survey_matrix[22,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[22,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q20_norm if wave==2 & online==1, detail

                {txt}Strategic to keep negotiating
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,516
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,516

{txt}50%    {res}       .5                      {txt}Mean          {res} .4302441
                        {txt}Largest       Std. dev.     {res}  .336023
{txt}75%    {res}      .75              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1129114
{txt}95%    {res}        1              1       {txt}Skewness      {res} .1596109
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.763586
{txt}
{com}. matrix survey_matrix[23,1] = 8.25
{txt}
{com}. matrix survey_matrix[23,2] = r(mean)
{txt}
{com}. matrix survey_matrix[23,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[23,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q20_norm if wave==1 & online==0, detail

                {txt}Strategic to keep negotiating
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,784
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      2,784

{txt}50%    {res}      .25                      {txt}Mean          {res} .4179239
                        {txt}Largest       Std. dev.     {res} .3824767
{txt}75%    {res}      .75              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1462884
{txt}95%    {res}        1              1       {txt}Skewness      {res} .2380765
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.498857
{txt}
{com}. matrix survey_matrix[24,1] = 8.5
{txt}
{com}. matrix survey_matrix[24,2] = r(mean)
{txt}
{com}. matrix survey_matrix[24,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[24,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. 
. svmat survey_matrix, names(sm_)
{txt}
{com}. 
. tw (pccapsym sm_3 sm_1 sm_4 sm_1, msymbol(point) lwidth(medium)) (scatter sm_2 sm_1 if sm_1==1 | sm_1==2 | sm_1==3 | sm_1==4 | sm_1==5 | sm_1==6 | sm_1==7 | sm_1==8, msymbol(oh)) (scatter sm_2 sm_1 if sm_1==1.25 | sm_1==2.25 | sm_1==3.25 | sm_1==4.25 | sm_1==5.25 | sm_1==6.25 | sm_1==7.25 | sm_1==8.25, msymbol(t))  (scatter sm_2 sm_1 if sm_1==1.5 | sm_1==2.5 | sm_1==3.5 | sm_1==4.5 | sm_1==5.5 | sm_1==6.5 | sm_1==7.5 | sm_1==8.5, msymbol(d)), ///
> xtitle("")  ytitle("Mean public support") xlabel(1.25 "UKR shouldn't negotiate" 2.25 "Morally wrong to sell out" 3.25 "RUS cannot be trusted" 4.25 "RUS will exploit peace" 5.25 "Peace is morally right" 6.25 "UKR must make peace" 7.25 "UKR must make terr. concessions" 8.25 "Strategic to keep negotiating", angle(60)) legend(order( 2 "Online wave 1" 3 "Online wave 2" 4 "Telephone wave 1") position(2) bmargin(medium))
{res}{txt}
{com}. 
. quietly graph export figure_a4a.jpg, replace
{txt}
{com}. 
. *A-4b: Terms of Peace
. 
. drop sm_*
{txt}
{com}. 
. matrix survey_matrix = J(29,4,.)        
{txt}
{com}.                 
. summarize q21_norm if wave==1 & online==1, detail

                {txt}Ukraine should not join NATO
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,529
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,529

{txt}50%    {res}       .3                      {txt}Mean          {res}  .318378
                        {txt}Largest       Std. dev.     {res} .3339961
{txt}75%    {res}       .5              1
{txt}90%    {res}       .9              1       {txt}Variance      {res} .1115534
{txt}95%    {res}        1              1       {txt}Skewness      {res} .6288582
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.210409
{txt}
{com}. matrix survey_matrix[1,1] = 1
{txt}
{com}. matrix survey_matrix[1,2] = r(mean)
{txt}
{com}. matrix survey_matrix[1,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[1,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q21_norm if wave==2 & online==1, detail

                {txt}Ukraine should not join NATO
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,602
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,602

{txt}50%    {res}       .3                      {txt}Mean          {res} .3245943
                        {txt}Largest       Std. dev.     {res}  .333429
{txt}75%    {res}       .5              1
{txt}90%    {res}       .9              1       {txt}Variance      {res} .1111749
{txt}95%    {res}        1              1       {txt}Skewness      {res} .5908005
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.193228
{txt}
{com}. matrix survey_matrix[2,1] = 1.25
{txt}
{com}. matrix survey_matrix[2,2] = r(mean)
{txt}
{com}. matrix survey_matrix[2,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[2,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q21_norm if wave==1 & online==0, detail

                {txt}Ukraine should not join NATO
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,794
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      2,794

{txt}50%    {res}        0                      {txt}Mean          {res} .3119184
                        {txt}Largest       Std. dev.     {res} .3675979
{txt}75%    {res}       .5              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1351282
{txt}95%    {res}        1              1       {txt}Skewness      {res} .7279702
{txt}99%    {res}        1              1       {txt}Kurtosis      {res}  2.12161
{txt}
{com}. matrix survey_matrix[3,1] = 1.5
{txt}
{com}. matrix survey_matrix[3,2] = r(mean)
{txt}
{com}. matrix survey_matrix[3,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[3,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q22_norm if wave==1 & online==1, detail

             {txt}Support western security guarantees
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}       .4              0
{txt}10%    {res}       .5              0       {txt}Obs         {res}      1,558
{txt}25%    {res}       .7              0       {txt}Sum of wgt. {res}      1,558

{txt}50%    {res}        1                      {txt}Mean          {res} .8439666
                        {txt}Largest       Std. dev.     {res} .2458105
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0604228
{txt}95%    {res}        1              1       {txt}Skewness      {res} -1.73243
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 5.509383
{txt}
{com}. matrix survey_matrix[4,1] = 2
{txt}
{com}. matrix survey_matrix[4,2] = r(mean)
{txt}
{com}. matrix survey_matrix[4,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[4,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q22_norm if wave==2 & online==1, detail

             {txt}Support western security guarantees
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}       .4              0
{txt}10%    {res}       .5              0       {txt}Obs         {res}      1,576
{txt}25%    {res}       .7              0       {txt}Sum of wgt. {res}      1,576

{txt}50%    {res}        1                      {txt}Mean          {res} .8340102
                        {txt}Largest       Std. dev.     {res} .2481641
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0615854
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.620071
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 5.151385
{txt}
{com}. matrix survey_matrix[5,1] = 2.25
{txt}
{com}. matrix survey_matrix[5,2] = r(mean)
{txt}
{com}. matrix survey_matrix[5,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[5,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q22_norm if wave==1 & online==0, detail

             {txt}Support western security guarantees
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}       .2              0
{txt}10%    {res}       .5              0       {txt}Obs         {res}      2,899
{txt}25%    {res}       .8              0       {txt}Sum of wgt. {res}      2,899

{txt}50%    {res}        1                      {txt}Mean          {res} .8614005
                        {txt}Largest       Std. dev.     {res} .2615653
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0684164
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.995048
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 6.194811
{txt}
{com}. matrix survey_matrix[6,1] = 2.5
{txt}
{com}. matrix survey_matrix[6,2] = r(mean)
{txt}
{com}. matrix survey_matrix[6,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[6,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q23_norm if wave==1 & online==1, detail

            {txt}Support Russian as official language
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,640
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,640

{txt}50%    {res}       .2                      {txt}Mean          {res} .3339024
                        {txt}Largest       Std. dev.     {res} .3755614
{txt}75%    {res}       .6              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1410464
{txt}95%    {res}        1              1       {txt}Skewness      {res} .6148574
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.875796
{txt}
{com}. matrix survey_matrix[7,1] = 3
{txt}
{com}. matrix survey_matrix[7,2] = r(mean)
{txt}
{com}. matrix survey_matrix[7,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[7,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q23_norm if wave==2 & online==1, detail

            {txt}Support Russian as official language
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,667
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,667

{txt}50%    {res}       .2                      {txt}Mean          {res} .3464307
                        {txt}Largest       Std. dev.     {res} .3773973
{txt}75%    {res}       .7              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1424287
{txt}95%    {res}        1              1       {txt}Skewness      {res} .5498061
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.785935
{txt}
{com}. matrix survey_matrix[8,1] = 3.25
{txt}
{com}. matrix survey_matrix[8,2] = r(mean)
{txt}
{com}. matrix survey_matrix[8,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[8,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q23_norm if wave==1 & online==0, detail

            {txt}Support Russian as official language
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,948
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      2,948

{txt}50%    {res}       .1                      {txt}Mean          {res} .3523066
                        {txt}Largest       Std. dev.     {res} .3954244
{txt}75%    {res}       .7              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1563605
{txt}95%    {res}        1              1       {txt}Skewness      {res} .5547654
{txt}99%    {res}        1              1       {txt}Kurtosis      {res}  1.73013
{txt}
{com}. matrix survey_matrix[9,1] = 3.5
{txt}
{com}. matrix survey_matrix[9,2] = r(mean)
{txt}
{com}. matrix survey_matrix[9,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[9,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q24_norm if wave==1 & online==1, detail

              {txt}Support Crimea as part of Russia
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,601
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,601

{txt}50%    {res}        0                      {txt}Mean          {res} .1109931
                        {txt}Largest       Std. dev.     {res} .2386767
{txt}75%    {res}        0              1
{txt}90%    {res}       .5              1       {txt}Variance      {res} .0569666
{txt}95%    {res}       .7              1       {txt}Skewness      {res} 2.225881
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 7.125039
{txt}
{com}. matrix survey_matrix[10,1] = 4
{txt}
{com}. matrix survey_matrix[10,2] = r(mean)
{txt}
{com}. matrix survey_matrix[10,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[10,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q24_norm if wave==2 & online==1, detail

              {txt}Support Crimea as part of Russia
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,636
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,636

{txt}50%    {res}        0                      {txt}Mean          {res} .1285452
                        {txt}Largest       Std. dev.     {res} .2501094
{txt}75%    {res}       .1              1
{txt}90%    {res}       .5              1       {txt}Variance      {res} .0625547
{txt}95%    {res}       .7              1       {txt}Skewness      {res}  1.92739
{txt}99%    {res}        1              1       {txt}Kurtosis      {res}  5.76785
{txt}
{com}. matrix survey_matrix[11,1] = 4.25
{txt}
{com}. matrix survey_matrix[11,2] = r(mean)
{txt}
{com}. matrix survey_matrix[11,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[11,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q24_norm if wave==1 & online==0, detail

              {txt}Support Crimea as part of Russia
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,927
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      2,927

{txt}50%    {res}        0                      {txt}Mean          {res} .0916638
                        {txt}Largest       Std. dev.     {res} .2349915
{txt}75%    {res}        0              1
{txt}90%    {res}       .5              1       {txt}Variance      {res}  .055221
{txt}95%    {res}       .6              1       {txt}Skewness      {res} 2.692239
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 9.402502
{txt}
{com}. matrix survey_matrix[12,1] = 4.5
{txt}
{com}. matrix survey_matrix[12,2] = r(mean)
{txt}
{com}. matrix survey_matrix[12,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[12,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q25_norm if wave==1 & online==1, detail

            {txt}Support DNR/LNR as independent states
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,572
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,572

{txt}50%    {res}        0                      {txt}Mean          {res}  .143257
                        {txt}Largest       Std. dev.     {res}   .26061
{txt}75%    {res}       .2              1
{txt}90%    {res}       .5              1       {txt}Variance      {res} .0679176
{txt}95%    {res}       .7              1       {txt}Skewness      {res} 1.751444
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 5.065838
{txt}
{com}. matrix survey_matrix[13,1] = 5
{txt}
{com}. matrix survey_matrix[13,2] = r(mean)
{txt}
{com}. matrix survey_matrix[13,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[13,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q25_norm if wave==2 & online==1, detail

            {txt}Support DNR/LNR as independent states
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,594
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,594

{txt}50%    {res}        0                      {txt}Mean          {res} .1482434
                        {txt}Largest       Std. dev.     {res} .2583934
{txt}75%    {res}       .2              1
{txt}90%    {res}       .5              1       {txt}Variance      {res} .0667672
{txt}95%    {res}       .7              1       {txt}Skewness      {res} 1.651827
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 4.783376
{txt}
{com}. matrix survey_matrix[14,1] = 5.25
{txt}
{com}. matrix survey_matrix[14,2] = r(mean)
{txt}
{com}. matrix survey_matrix[14,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[14,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q25_norm if wave==1 & online==0, detail

            {txt}Support DNR/LNR as independent states
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,860
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      2,860

{txt}50%    {res}        0                      {txt}Mean          {res} .1146154
                        {txt}Largest       Std. dev.     {res} .2518272
{txt}75%    {res}        0              1
{txt}90%    {res}       .5              1       {txt}Variance      {res}  .063417
{txt}95%    {res}       .7              1       {txt}Skewness      {res} 2.229531
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 7.066519
{txt}
{com}. matrix survey_matrix[15,1] = 5.5
{txt}
{com}. matrix survey_matrix[15,2] = r(mean)
{txt}
{com}. matrix survey_matrix[15,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[15,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q26_norm if wave==1 & online==1, detail

            {txt}Support reducing size of Ukr military
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,572
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,572

{txt}50%    {res}        0                      {txt}Mean          {res}  .109542
                        {txt}Largest       Std. dev.     {res} .2211262
{txt}75%    {res}       .1              1
{txt}90%    {res}       .5              1       {txt}Variance      {res} .0488968
{txt}95%    {res}       .5              1       {txt}Skewness      {res} 2.116958
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 6.860406
{txt}
{com}. matrix survey_matrix[16,1] = 6
{txt}
{com}. matrix survey_matrix[16,2] = r(mean)
{txt}
{com}. matrix survey_matrix[16,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[16,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q26_norm if wave==2 & online==1, detail

            {txt}Support reducing size of Ukr military
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,610
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,610

{txt}50%    {res}        0                      {txt}Mean          {res} .1188199
                        {txt}Largest       Std. dev.     {res} .2303829
{txt}75%    {res}       .1              1
{txt}90%    {res}       .5              1       {txt}Variance      {res} .0530763
{txt}95%    {res}       .5              1       {txt}Skewness      {res} 2.034144
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 6.557554
{txt}
{com}. matrix survey_matrix[17,1] = 6.25
{txt}
{com}. matrix survey_matrix[17,2] = r(mean)
{txt}
{com}. matrix survey_matrix[17,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[17,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q26_norm if wave==1 & online==0, detail

            {txt}Support reducing size of Ukr military
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,895
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      2,895

{txt}50%    {res}        0                      {txt}Mean          {res} .0957513
                        {txt}Largest       Std. dev.     {res} .2439862
{txt}75%    {res}        0              1
{txt}90%    {res}       .5              1       {txt}Variance      {res} .0595293
{txt}95%    {res}       .7              1       {txt}Skewness      {res} 2.666414
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 9.174211
{txt}
{com}. matrix survey_matrix[18,1] = 6.5
{txt}
{com}. matrix survey_matrix[18,2] = r(mean)
{txt}
{com}. matrix survey_matrix[18,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[18,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q27_norm if wave==1 & online==1, detail

                  {txt}Support voting by DNR/LNR
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,573
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,573

{txt}50%    {res}       .2                      {txt}Mean          {res} .3438017
                        {txt}Largest       Std. dev.     {res} .3757538
{txt}75%    {res}       .6              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1411909
{txt}95%    {res}        1              1       {txt}Skewness      {res} .5586618
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.806157
{txt}
{com}. matrix survey_matrix[19,1] = 7
{txt}
{com}. matrix survey_matrix[19,2] = r(mean)
{txt}
{com}. matrix survey_matrix[19,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[19,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q27_norm if wave==2 & online==1, detail

                  {txt}Support voting by DNR/LNR
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,605
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,605

{txt}50%    {res}       .2                      {txt}Mean          {res} .3452336
                        {txt}Largest       Std. dev.     {res} .3722581
{txt}75%    {res}       .6              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1385761
{txt}95%    {res}        1              1       {txt}Skewness      {res} .5466312
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.825767
{txt}
{com}. matrix survey_matrix[20,1] = 7.25
{txt}
{com}. matrix survey_matrix[20,2] = r(mean)
{txt}
{com}. matrix survey_matrix[20,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[20,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q27_norm if wave==1 & online==0, detail

                  {txt}Support voting by DNR/LNR
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,805
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      2,805

{txt}50%    {res}       .2                      {txt}Mean          {res} .3604278
                        {txt}Largest       Std. dev.     {res} .4019955
{txt}75%    {res}       .7              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .1616004
{txt}95%    {res}        1              1       {txt}Skewness      {res} .5356693
{txt}99%    {res}        1              1       {txt}Kurtosis      {res}  1.68453
{txt}
{com}. matrix survey_matrix[21,1] = 7.5
{txt}
{com}. matrix survey_matrix[21,2] = r(mean)
{txt}
{com}. matrix survey_matrix[21,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[21,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q28_norm if wave==1 & online==1, detail

            {txt}Support Ukraine rejecting joining EU
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,579
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,579

{txt}50%    {res}        0                      {txt}Mean          {res} .1658645
                        {txt}Largest       Std. dev.     {res}  .265018
{txt}75%    {res}       .3              1
{txt}90%    {res}       .5              1       {txt}Variance      {res} .0702345
{txt}95%    {res}       .7              1       {txt}Skewness      {res} 1.489699
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 4.308513
{txt}
{com}. matrix survey_matrix[22,1] = 8
{txt}
{com}. matrix survey_matrix[22,2] = r(mean)
{txt}
{com}. matrix survey_matrix[22,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[22,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q28_norm if wave==2 & online==1, detail

            {txt}Support Ukraine rejecting joining EU
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,616
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,616

{txt}50%    {res}        0                      {txt}Mean          {res} .1820545
                        {txt}Largest       Std. dev.     {res} .2713338
{txt}75%    {res}       .4              1
{txt}90%    {res}       .5              1       {txt}Variance      {res}  .073622
{txt}95%    {res}       .8              1       {txt}Skewness      {res}  1.36944
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.944835
{txt}
{com}. matrix survey_matrix[23,1] = 8.25
{txt}
{com}. matrix survey_matrix[23,2] = r(mean)
{txt}
{com}. matrix survey_matrix[23,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[23,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q28_norm if wave==1 & online==0, detail

            {txt}Support Ukraine rejecting joining EU
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,886
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      2,886

{txt}50%    {res}        0                      {txt}Mean          {res} .1770963
                        {txt}Largest       Std. dev.     {res} .3099148
{txt}75%    {res}       .3              1
{txt}90%    {res}       .5              1       {txt}Variance      {res} .0960472
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.591581
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 4.263623
{txt}
{com}. matrix survey_matrix[24,1] = 8.5
{txt}
{com}. matrix survey_matrix[24,2] = r(mean)
{txt}
{com}. matrix survey_matrix[24,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[24,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. 
. summarize q29_norm if wave==1 & online==1, detail

           {txt}Support Zelensky stepping down as prez
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,573
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,573

{txt}50%    {res}        0                      {txt}Mean          {res} .2393516
                        {txt}Largest       Std. dev.     {res} .3257152
{txt}75%    {res}       .5              1
{txt}90%    {res}       .8              1       {txt}Variance      {res} .1060904
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.122001
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.006094
{txt}
{com}. matrix survey_matrix[25,1] = 9
{txt}
{com}. matrix survey_matrix[25,2] = r(mean)
{txt}
{com}. matrix survey_matrix[25,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[25,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q29_norm if wave==2 & online==1, detail

           {txt}Support Zelensky stepping down as prez
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,621
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,621

{txt}50%    {res}        0                      {txt}Mean          {res}   .23905
                        {txt}Largest       Std. dev.     {res} .3166161
{txt}75%    {res}       .5              1
{txt}90%    {res}       .7              1       {txt}Variance      {res} .1002458
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.096018
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.043524
{txt}
{com}. matrix survey_matrix[26,1] = 9.25
{txt}
{com}. matrix survey_matrix[26,2] = r(mean)
{txt}
{com}. matrix survey_matrix[26,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[26,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize q29_norm if wave==1 & online==0, detail

           {txt}Support Zelensky stepping down as prez
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,891
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      2,891

{txt}50%    {res}        0                      {txt}Mean          {res} .1620547
                        {txt}Largest       Std. dev.     {res} .3048781
{txt}75%    {res}       .2              1
{txt}90%    {res}       .5              1       {txt}Variance      {res} .0929507
{txt}95%    {res}        1              1       {txt}Skewness      {res} 1.757482
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 4.785287
{txt}
{com}. matrix survey_matrix[27,1] = 9.5
{txt}
{com}. matrix survey_matrix[27,2] = r(mean)
{txt}
{com}. matrix survey_matrix[27,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[27,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. 
. svmat survey_matrix, names(sm_)
{txt}
{com}. 
. tw (pccapsym sm_3 sm_1 sm_4 sm_1, msymbol(point) lwidth(medium)) (scatter sm_2 sm_1 if sm_1==1 | sm_1==2 | sm_1==3 | sm_1==4 | sm_1==5 | sm_1==6 | sm_1==7 | sm_1==8 | sm_1==9, msymbol(oh)) (scatter sm_2 sm_1 if sm_1==1.25 | sm_1==2.25 | sm_1==3.25 | sm_1==4.25 | sm_1==5.25 | sm_1==6.25 | sm_1==7.25 | sm_1==8.25, msymbol(t))  (scatter sm_2 sm_1 if sm_1==1.5 | sm_1==2.5 | sm_1==3.5 | sm_1==4.5 | sm_1==5.5 | sm_1==6.5 | sm_1==7.5 | sm_1==8.5 | sm_1==9.5, msymbol(d)), ///
> xtitle("")  ytitle("Mean public support") xlabel(1.25 "UKR shouldn't join NATO" 2.25 "Western security guarantees" 3.25 "Russian as official language" 4.25 "Crimea as part of Russia" 5.25 "Independent DNR/LNR" 6.25 "Reduce UKR army size" 7.25 "Voting by DNR/LNR" 8.25 "UKR reject joining EU" 9.25 "Zelensky stepping down", angle(60)) legend(order( 2 "Online wave 1" 3 "Online wave 2" 4 "Telephone wave 1") position(2) bmargin(medium))
{res}{txt}
{com}. 
. quietly graph export figure_a4b.jpg, replace
{txt}
{com}. 
. ***FIGURE A-8: What explained Ukraine's military success
. 
. drop sm_* 
{txt}
{com}. 
. matrix survey_matrix = J(10,4,.)        
{txt}
{com}.                 
. summarize milsuccess_q9_norm if wave==1 & online==1, detail

                     {txt}milsuccess_q9_norm
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}       .5              0
{txt}10%    {res}       .5              0       {txt}Obs         {res}      1,630
{txt}25%    {res}       .8              0       {txt}Sum of wgt. {res}      1,630

{txt}50%    {res}        1                      {txt}Mean          {res} .8661963
                        {txt}Largest       Std. dev.     {res} .2036008
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0414533
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.743981
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 6.036531
{txt}
{com}. matrix survey_matrix[1,1] = 1
{txt}
{com}. matrix survey_matrix[1,2] = r(mean)
{txt}
{com}. matrix survey_matrix[1,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[1,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize milsuccess_q9_norm if wave==1 & telephone==1, detail

                     {txt}milsuccess_q9_norm
{hline 61}
      Percentiles      Smallest
 1%    {res}       .5              0
{txt} 5%    {res}       .5              0
{txt}10%    {res}       .7              0       {txt}Obs         {res}      2,923
{txt}25%    {res}       .9              0       {txt}Sum of wgt. {res}      2,923

{txt}50%    {res}        1                      {txt}Mean          {res} .9189189
                        {txt}Largest       Std. dev.     {res} .1605131
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0257645
{txt}95%    {res}        1              1       {txt}Skewness      {res}-2.341643
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 8.976993
{txt}
{com}. matrix survey_matrix[2,1] = 1.25
{txt}
{com}. matrix survey_matrix[2,2] = r(mean)
{txt}
{com}. matrix survey_matrix[2,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[2,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize milsuccess_q10_norm if wave==1 & online==1, detail

                     {txt}milsuccess_q10_norm
{hline 61}
      Percentiles      Smallest
 1%    {res}       .1              0
{txt} 5%    {res}       .5              0
{txt}10%    {res}       .5              0       {txt}Obs         {res}      1,616
{txt}25%    {res}       .7              0       {txt}Sum of wgt. {res}      1,616

{txt}50%    {res}       .9                      {txt}Mean          {res} .8214728
                        {txt}Largest       Std. dev.     {res} .2150454
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0462445
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.137422
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.900155
{txt}
{com}. matrix survey_matrix[3,1] = 1.75
{txt}
{com}. matrix survey_matrix[3,2] = r(mean)
{txt}
{com}. matrix survey_matrix[3,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[3,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize milsuccess_q10_norm if wave==1 & telephone==1, detail

                     {txt}milsuccess_q10_norm
{hline 61}
      Percentiles      Smallest
 1%    {res}       .3              0
{txt} 5%    {res}       .5              0
{txt}10%    {res}       .5              0       {txt}Obs         {res}      2,797
{txt}25%    {res}       .8              0       {txt}Sum of wgt. {res}      2,797

{txt}50%    {res}        1                      {txt}Mean          {res} .8895245
                        {txt}Largest       Std. dev.     {res} .1885331
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0355447
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.751208
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 5.578703
{txt}
{com}. matrix survey_matrix[4,1] = 2
{txt}
{com}. matrix survey_matrix[4,2] = r(mean)
{txt}
{com}. matrix survey_matrix[4,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[4,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize milsuccess_q11_norm if wave==1 & online==1, detail

                     {txt}milsuccess_q11_norm
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}       .5              0
{txt}10%    {res}       .5              0       {txt}Obs         {res}      1,617
{txt}25%    {res}       .6              0       {txt}Sum of wgt. {res}      1,617

{txt}50%    {res}       .9                      {txt}Mean          {res} .7956092
                        {txt}Largest       Std. dev.     {res} .2257534
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0509646
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.9209637
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.255496
{txt}
{com}. matrix survey_matrix[5,1] = 2.5
{txt}
{com}. matrix survey_matrix[5,2] = r(mean)
{txt}
{com}. matrix survey_matrix[5,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[5,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize milsuccess_q11_norm if wave==1 & telephone==1, detail

                     {txt}milsuccess_q11_norm
{hline 61}
      Percentiles      Smallest
 1%    {res}       .1              0
{txt} 5%    {res}       .5              0
{txt}10%    {res}       .5              0       {txt}Obs         {res}      2,731
{txt}25%    {res}       .6              0       {txt}Sum of wgt. {res}      2,731

{txt}50%    {res}       .9                      {txt}Mean          {res} .8064445
                        {txt}Largest       Std. dev.     {res} .2279279
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0519511
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.9090489
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.072688
{txt}
{com}. matrix survey_matrix[6,1] = 2.75
{txt}
{com}. matrix survey_matrix[6,2] = r(mean)
{txt}
{com}. matrix survey_matrix[6,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[6,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize milsuccess_q12_norm if wave==1 & online==1, detail

                     {txt}milsuccess_q12_norm
{hline 61}
      Percentiles      Smallest
 1%    {res}       .2              0
{txt} 5%    {res}       .5              0
{txt}10%    {res}       .5              0       {txt}Obs         {res}      1,638
{txt}25%    {res}       .8              0       {txt}Sum of wgt. {res}      1,638

{txt}50%    {res}        1                      {txt}Mean          {res} .8808303
                        {txt}Largest       Std. dev.     {res}  .197012
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0388137
{txt}95%    {res}        1              1       {txt}Skewness      {res}-1.763189
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 5.809363
{txt}
{com}. matrix survey_matrix[7,1] = 3.25
{txt}
{com}. matrix survey_matrix[7,2] = r(mean)
{txt}
{com}. matrix survey_matrix[7,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[7,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize milsuccess_q12_norm if wave==1 & telephone==1, detail

                     {txt}milsuccess_q12_norm
{hline 61}
      Percentiles      Smallest
 1%    {res}       .4              0
{txt} 5%    {res}       .5              0
{txt}10%    {res}       .7              0       {txt}Obs         {res}      2,934
{txt}25%    {res}       .9              0       {txt}Sum of wgt. {res}      2,934

{txt}50%    {res}        1                      {txt}Mean          {res} .9193592
                        {txt}Largest       Std. dev.     {res} .1668999
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0278556
{txt}95%    {res}        1              1       {txt}Skewness      {res}-2.452074
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 9.480558
{txt}
{com}. matrix survey_matrix[8,1] = 3.5
{txt}
{com}. matrix survey_matrix[8,2] = r(mean)
{txt}
{com}. matrix survey_matrix[8,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[8,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize milsuccess_q13_norm if wave==1 & online==1, detail

                     {txt}milsuccess_q13_norm
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}       .2              0
{txt}10%    {res}       .3              0       {txt}Obs         {res}      1,516
{txt}25%    {res}       .5              0       {txt}Sum of wgt. {res}      1,516

{txt}50%    {res}       .5                      {txt}Mean          {res} .6204486
                        {txt}Largest       Std. dev.     {res} .2610794
{txt}75%    {res}       .8              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0681624
{txt}95%    {res}        1              1       {txt}Skewness      {res} -.151229
{txt}99%    {res}        1              1       {txt}Kurtosis      {res}   2.6774
{txt}
{com}. matrix survey_matrix[9,1] = 4
{txt}
{com}. matrix survey_matrix[9,2] = r(mean)
{txt}
{com}. matrix survey_matrix[9,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[9,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. summarize milsuccess_q13_norm if wave==1 & telephone==1, detail

                     {txt}milsuccess_q13_norm
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,658
{txt}25%    {res}       .5              0       {txt}Sum of wgt. {res}      2,658

{txt}50%    {res}       .5                      {txt}Mean          {res}  .574605
                        {txt}Largest       Std. dev.     {res} .3041773
{txt}75%    {res}       .8              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0925238
{txt}95%    {res}        1              1       {txt}Skewness      {res}  -.20013
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.408681
{txt}
{com}. matrix survey_matrix[10,1] = 4.25
{txt}
{com}. matrix survey_matrix[10,2] = r(mean)
{txt}
{com}. matrix survey_matrix[10,3] = r(mean)-1.96*r(sd)/sqrt(r(N))
{txt}
{com}. matrix survey_matrix[10,4] = r(mean)+1.96*r(sd)/sqrt(r(N))
{txt}
{com}. 
. svmat survey_matrix, names(sm_)
{txt}
{com}. 
. tw (pccapsym sm_3 sm_1 sm_4 sm_1, msymbol(point) lwidth(medium)) (scatter sm_2 sm_1 if sm_1==1 | sm_1==1.75 | sm_1==2.5 | sm_1==3.25 | sm_1==4, msymbol(oh)) (scatter sm_2 sm_1 if sm_1==1.25 | sm_1==2 | sm_1==2.75 | sm_1==3.5 | sm_1==4.25, msymbol(t)), ///
> xtitle("")  ytitle("Ukraine's army success due to") xlabel(1.12 "Ukrainian soldiers" 1.87 "Drones" 2.62 "Western weapons" 3.37 "Ukrainian people" 4.12 "Russian army low quality", angle(60)) legend(order( 2 "Online wave 1" 3 "Telephone wave 1") position(2) bmargin(medium))
{res}{txt}
{com}. 
. quietly graph export figure_a8.jpg, replace
{txt}
{com}. 
. ***FIGURE A-9: Baseline support for agreement decreases in 2023 relative to 2022
. 
. preserve
{txt}
{com}. 
. drop wave2
{txt}
{com}. gen wave2=1
{txt}
{com}. replace wave2=2 if wave==2 & online==1
{txt}(1,729 real changes made)

{com}. replace wave2=3 if wave==1 & online==0
{txt}(3,016 real changes made)

{com}. replace wave2=4 if wave==2 & online==0  
{txt}(2,013 real changes made)

{com}.         
. 
. 
. **ADJUSTING VARIABLES IN OMNIBUS (some variables with the same name have different meaning in omnibus compared to the other waves...)
. replace q2=q2_gender if telephone==1 & wave==2
{txt}(1,981 real changes made)

{com}. replace q1=q1_age  if telephone==1 & wave==2
{txt}(2,013 real changes made)

{com}. replace q4=q4_language if telephone==1 & wave==2
{txt}(1,799 real changes made)

{com}. replace q3=q3_educ if telephone==1 & wave==2
{txt}(1,971 real changes made)

{com}. 
.                 
. eststo m1: reg baseline_support ib3.wave2, vce(robust)

{txt}Linear regression                               Number of obs     = {res}     6,257
                                                {txt}F(2, 6254)        =  {res}    46.93
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0151
                                                {txt}Root MSE          =    {res} .40515

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}baseline_s~t{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}wave2 {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0233786{col 26}{space 2} .0120902{col 37}{space 1}    1.93{col 46}{space 3}0.053{col 54}{space 4}-.0003223{col 67}{space 3} .0470795
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0996188{col 26}{space 2} .0125148{col 37}{space 1}   -7.96{col 46}{space 3}0.000{col 54}{space 4}-.1241522{col 67}{space 3}-.0750854
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .6028011{col 26}{space 2} .0077265{col 37}{space 1}   78.02{col 46}{space 3}0.000{col 54}{space 4} .5876545{col 67}{space 3} .6179478
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo m2: reghdfe baseline_support ib3.wave2 q1, a(oblast_feb24 i.q2 i.q3 i.q4 ) vce(cluster oblast_feb24)
{res}{txt}(dropped 1 {browse "http://scorreia.com/research/singletons.pdf":singleton observations})
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 7 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     6,256
{txt}Absorbing 4 HDFE groups{col 51}F({res}   3{txt},{res}     24{txt}){col 67}= {res}     38.85
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0000
{txt}{col 51}R-squared{col 67}= {res}    0.0657
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0592
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0154
{txt}{col 1}Number of clusters ({res}oblast_feb24{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.3959

{txt}{ralign 78:(Std. err. adjusted for {res:25} clusters in {res:oblast_feb24})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}baseline_s~t{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}wave2 {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0176858{col 26}{space 2} .0152864{col 37}{space 1}    1.16{col 46}{space 3}0.259{col 54}{space 4}-.0138638{col 67}{space 3} .0492353
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0787909{col 26}{space 2} .0128259{col 37}{space 1}   -6.14{col 46}{space 3}0.000{col 54}{space 4}-.1052622{col 67}{space 3}-.0523196
{txt}{space 12} {c |}
{space 10}q1 {c |}{col 14}{res}{space 2}-.0019391{col 26}{space 2} .0003945{col 37}{space 1}   -4.91{col 46}{space 3}0.000{col 54}{space 4}-.0027533{col 67}{space 3}-.0011248
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6859773{col 26}{space 2} .0198547{col 37}{space 1}   34.55{col 46}{space 3}0.000{col 54}{space 4} .6449991{col 67}{space 3} .7269554
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 14}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}  Absorbed FE{col 15}{c |} Categories{col 28} - Redundant{col 40}  = Num. Coefs{col 55}{c |}
{res}{col 1}{text}{hline 14}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} oblast_feb24{col 15}{c |}{space 1}       25{col 28}{space 1}       25{col 40}{result}{space 1}        0{col 54}{text}*{col 55}{c |}
{res}{col 1}{text}           q2{col 15}{c |}{space 1}        2{col 28}{space 1}        1{col 40}{result}{space 1}        1{col 54}{text} {col 55}{c |}
{res}{col 1}{text}           q3{col 15}{c |}{space 1}       10{col 28}{space 1}        1{col 40}{result}{space 1}        9{col 54}{text} {col 55}{c |}
{res}{col 1}{text}           q4{col 15}{c |}{space 1}        7{col 28}{space 1}        1{col 40}{result}{space 1}        6{col 54}{text}?{col 55}{c |}
{res}{col 1}{text}{hline 14}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}
{com}.         
. 
. coefplot (m1, label(No controls)) (m2, label(With controls)), vertical keep(1.wave2 4.wave2) xlabel(1 `" "Online wave 1" "June 2022" "' 2 `" "Telephone wave 2" "May 2023" "' ,angle(45))  levels(95 90) title("Support for settlement with Russia (relative to telephone wave 1 in June 2022)", size(medium))
{res}{txt}
{com}. 
. quietly graph export figure_a9.jpg, replace
{txt}
{com}. 
. eststo clear 
{txt}
{com}. restore
{txt}
{com}. 
. ***FIGURE A-10: Self reported exposure and recorded combat activity in region
. ***REQUIRES A SEPARATE DATASET SEE THE LINE 1002
. 
. 
. 
. ***FIGURE A-11: First-difference estimates of relationship between violence exposure and attitudes, online panel – by oblast
. 
. *A-11a: Negotiating pronciples
. forvalues i = 2(1)26 {c -(}
{txt}  2{com}. eststo m1`i': reghdfe fd_pca_principlesneg0_1 fd_pca_violexposure0_1 if oblast_lag==`i', vce( robust)           
{txt}  3{com}. coefplot m*, drop(_cons) vertical yline(0, lcolor(red)) levels(95 90) legend(off) ///
> coeflabels(m12= "City of Kyiv" m13= "Kyiv oblast"  m14= "Vinnytsia" m15= "Volyn" m16= "Dnipropetrovsk" m17= "Donetsk" m18= "Zhytomyr" m19= "Zakarpattya" m110= "Zaporizhzhya" m111= "Ivano-Frankivsk" m112= "Kirovohrad" m113= "Luhansk" m114= "Lviv" m115= "Mykolayiv" m116= "Odesa" m117= "Poltava" m118= "Rivne" m119= "Sumy" m120= "Ternopil" m121 = "Kharkiv" m122= "Kherson" m123= "Khmelnytskiy" m124= "Cherkasy" m125= "Chernivtsi" m126= "Chernihiv",   labsize(small) angle(45)) aseq swapnames
{txt}  4{com}.         {c )-}       
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       138
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}    136{txt}){col 67}= {res}      1.13
{txt}{col 51}Prob > F{col 67}= {res}    0.2901
{txt}{col 51}R-squared{col 67}= {res}    0.0108
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0036
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0108
{txt}{col 51}Root MSE{col 67}= {res}    0.1325

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0926475{col 36}{space 2} .0872393{col 47}{space 1}    1.06{col 56}{space 3}0.290{col 64}{space 4}-.0798736{col 77}{space 3} .2651686
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .3478305{col 36}{space 2} .0483849{col 47}{space 1}    7.19{col 56}{space 3}0.000{col 64}{space 4} .2521464{col 77}{space 3} .4435146
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}         8
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      6{txt}){col 67}= {res}      0.11
{txt}{col 51}Prob > F{col 67}= {res}    0.7557
{txt}{col 51}R-squared{col 67}= {res}    0.0119
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.1528
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0119
{txt}{col 51}Root MSE{col 67}= {res}    0.1128

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1345306{col 36}{space 2} .4131057{col 47}{space 1}    0.33{col 56}{space 3}0.756{col 64}{space 4}-.8763027{col 77}{space 3} 1.145364
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .3515543{col 36}{space 2} .2294294{col 47}{space 1}    1.53{col 56}{space 3}0.176{col 64}{space 4}-.2098393{col 77}{space 3} .9129479
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        24
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     22{txt}){col 67}= {res}      4.65
{txt}{col 51}Prob > F{col 67}= {res}    0.0423
{txt}{col 51}R-squared{col 67}= {res}    0.1440
{txt}{col 51}Adj R-squared{col 67}= {res}    0.1051
{txt}{col 51}Within R-sq.{col 67}= {res}    0.1440
{txt}{col 51}Root MSE{col 67}= {res}    0.1313

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.4720344{col 36}{space 2} .2189912{col 47}{space 1}   -2.16{col 56}{space 3}0.042{col 64}{space 4}-.9261944{col 77}{space 3}-.0178744
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}  .688027{col 36}{space 2} .1305895{col 47}{space 1}    5.27{col 56}{space 3}0.000{col 64}{space 4} .4172011{col 77}{space 3}  .958853
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        11
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      9{txt}){col 67}= {res}      0.01
{txt}{col 51}Prob > F{col 67}= {res}    0.9405
{txt}{col 51}R-squared{col 67}= {res}    0.0005
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.1105
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0005
{txt}{col 51}Root MSE{col 67}= {res}    0.0634

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0157365{col 36}{space 2} .2051246{col 47}{space 1}    0.08{col 56}{space 3}0.941{col 64}{space 4}-.4482875{col 77}{space 3} .4797605
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .3703503{col 36}{space 2} .1060597{col 47}{space 1}    3.49{col 56}{space 3}0.007{col 64}{space 4} .1304265{col 77}{space 3}  .610274
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        53
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     51{txt}){col 67}= {res}      0.39
{txt}{col 51}Prob > F{col 67}= {res}    0.5335
{txt}{col 51}R-squared{col 67}= {res}    0.0046
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0150
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0046
{txt}{col 51}Root MSE{col 67}= {res}    0.1250

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0952528{col 36}{space 2} .1519458{col 47}{space 1}   -0.63{col 56}{space 3}0.534{col 64}{space 4}-.4002967{col 77}{space 3} .2097911
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4267224{col 36}{space 2}  .087918{col 47}{space 1}    4.85{col 56}{space 3}0.000{col 64}{space 4} .2502196{col 77}{space 3} .6032252
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        29
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     27{txt}){col 67}= {res}      0.01
{txt}{col 51}Prob > F{col 67}= {res}    0.9182
{txt}{col 51}R-squared{col 67}= {res}    0.0005
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0365
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0005
{txt}{col 51}Root MSE{col 67}= {res}    0.0905

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} -.021035{col 36}{space 2} .2030089{col 47}{space 1}   -0.10{col 56}{space 3}0.918{col 64}{space 4}-.4375749{col 77}{space 3} .3955049
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .3962242{col 36}{space 2} .1046398{col 47}{space 1}    3.79{col 56}{space 3}0.001{col 64}{space 4} .1815211{col 77}{space 3} .6109273
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        23
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     21{txt}){col 67}= {res}      0.68
{txt}{col 51}Prob > F{col 67}= {res}    0.4180
{txt}{col 51}R-squared{col 67}= {res}    0.0334
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0126
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0334
{txt}{col 51}Root MSE{col 67}= {res}    0.1330

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.2844569{col 36}{space 2} .3442938{col 47}{space 1}   -0.83{col 56}{space 3}0.418{col 64}{space 4}-1.000455{col 77}{space 3} .4315413
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5539646{col 36}{space 2} .2024621{col 47}{space 1}    2.74{col 56}{space 3}0.012{col 64}{space 4} .1329216{col 77}{space 3} .9750076
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}         8
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      6{txt}){col 67}= {res}      2.78
{txt}{col 51}Prob > F{col 67}= {res}    0.1468
{txt}{col 51}R-squared{col 67}= {res}    0.1497
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0080
{txt}{col 51}Within R-sq.{col 67}= {res}    0.1497
{txt}{col 51}Root MSE{col 67}= {res}    0.0565

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1294522{col 36}{space 2} .0777028{col 47}{space 1}    1.67{col 56}{space 3}0.147{col 64}{space 4}-.0606796{col 77}{space 3}  .319584
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .2951347{col 36}{space 2} .0556898{col 47}{space 1}    5.30{col 56}{space 3}0.002{col 64}{space 4} .1588667{col 77}{space 3} .4314026
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        32
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     30{txt}){col 67}= {res}      1.26
{txt}{col 51}Prob > F{col 67}= {res}    0.2710
{txt}{col 51}R-squared{col 67}= {res}    0.0684
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0374
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0684
{txt}{col 51}Root MSE{col 67}= {res}    0.1464

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .4644525{col 36}{space 2} .4141217{col 47}{space 1}    1.12{col 56}{space 3}0.271{col 64}{space 4}-.3812969{col 77}{space 3} 1.310202
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .1544569{col 36}{space 2}  .224261{col 47}{space 1}    0.69{col 56}{space 3}0.496{col 64}{space 4}-.3035451{col 77}{space 3} .6124589
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        14
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     12{txt}){col 67}= {res}      1.80
{txt}{col 51}Prob > F{col 67}= {res}    0.2050
{txt}{col 51}R-squared{col 67}= {res}    0.0288
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0521
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0288
{txt}{col 51}Root MSE{col 67}= {res}    0.1263

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.1482201{col 36}{space 2} .1106063{col 47}{space 1}   -1.34{col 56}{space 3}0.205{col 64}{space 4}-.3892105{col 77}{space 3} .0927703
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5149939{col 36}{space 2} .0827489{col 47}{space 1}    6.22{col 56}{space 3}0.000{col 64}{space 4} .3346996{col 77}{space 3} .6952882
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        10
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      8{txt}){col 67}= {res}      0.78
{txt}{col 51}Prob > F{col 67}= {res}    0.4039
{txt}{col 51}R-squared{col 67}= {res}    0.0256
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0962
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0256
{txt}{col 51}Root MSE{col 67}= {res}    0.1257

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.2915864{col 36}{space 2} .3309159{col 47}{space 1}   -0.88{col 56}{space 3}0.404{col 64}{space 4} -1.05468{col 77}{space 3} .4715072
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5124136{col 36}{space 2} .1432852{col 47}{space 1}    3.58{col 56}{space 3}0.007{col 64}{space 4} .1819973{col 77}{space 3} .8428299
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        11
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      9{txt}){col 67}= {res}      2.03
{txt}{col 51}Prob > F{col 67}= {res}    0.1877
{txt}{col 51}R-squared{col 67}= {res}    0.0422
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0642
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0422
{txt}{col 51}Root MSE{col 67}= {res}    0.0538

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} -.079131{col 36}{space 2}  .055498{col 47}{space 1}   -1.43{col 56}{space 3}0.188{col 64}{space 4}-.2046763{col 77}{space 3} .0464142
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}  .453869{col 36}{space 2} .0401758{col 47}{space 1}   11.30{col 56}{space 3}0.000{col 64}{space 4}  .362985{col 77}{space 3}  .544753
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        45
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     43{txt}){col 67}= {res}      1.34
{txt}{col 51}Prob > F{col 67}= {res}    0.2538
{txt}{col 51}R-squared{col 67}= {res}    0.0576
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0357
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0576
{txt}{col 51}Root MSE{col 67}= {res}    0.1258

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .3005971{col 36}{space 2}   .25991{col 47}{space 1}    1.16{col 56}{space 3}0.254{col 64}{space 4}-.2235615{col 77}{space 3} .8247556
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .2295562{col 36}{space 2} .1434522{col 47}{space 1}    1.60{col 56}{space 3}0.117{col 64}{space 4}-.0597426{col 77}{space 3} .5188551
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        34
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     32{txt}){col 67}= {res}      0.46
{txt}{col 51}Prob > F{col 67}= {res}    0.5040
{txt}{col 51}R-squared{col 67}= {res}    0.0161
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0147
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0161
{txt}{col 51}Root MSE{col 67}= {res}    0.1104

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.1339281{col 36}{space 2} .1981543{col 47}{space 1}   -0.68{col 56}{space 3}0.504{col 64}{space 4}-.5375551{col 77}{space 3}  .269699
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4685366{col 36}{space 2} .1081442{col 47}{space 1}    4.33{col 56}{space 3}0.000{col 64}{space 4}  .248254{col 77}{space 3} .6888193
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        25
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     23{txt}){col 67}= {res}      0.37
{txt}{col 51}Prob > F{col 67}= {res}    0.5500
{txt}{col 51}R-squared{col 67}= {res}    0.0239
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0186
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0239
{txt}{col 51}Root MSE{col 67}= {res}    0.1072

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1313159{col 36}{space 2} .2164259{col 47}{space 1}    0.61{col 56}{space 3}0.550{col 64}{space 4}-.3163953{col 77}{space 3} .5790271
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .2934141{col 36}{space 2} .1209054{col 47}{space 1}    2.43{col 56}{space 3}0.023{col 64}{space 4} .0433022{col 77}{space 3}  .543526
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        24
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     22{txt}){col 67}= {res}      0.01
{txt}{col 51}Prob > F{col 67}= {res}    0.9425
{txt}{col 51}R-squared{col 67}= {res}    0.0003
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0452
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0003
{txt}{col 51}Root MSE{col 67}= {res}    0.1464

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0353372{col 36}{space 2} .4844352{col 47}{space 1}   -0.07{col 56}{space 3}0.943{col 64}{space 4}-1.039994{col 77}{space 3} .9693199
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4341086{col 36}{space 2} .2427779{col 47}{space 1}    1.79{col 56}{space 3}0.088{col 64}{space 4} -.069382{col 77}{space 3} .9375991
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        10
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      8{txt}){col 67}= {res}      1.33
{txt}{col 51}Prob > F{col 67}= {res}    0.2819
{txt}{col 51}R-squared{col 67}= {res}    0.2093
{txt}{col 51}Adj R-squared{col 67}= {res}    0.1104
{txt}{col 51}Within R-sq.{col 67}= {res}    0.2093
{txt}{col 51}Root MSE{col 67}= {res}    0.0742

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.4456752{col 36}{space 2} .3862912{col 47}{space 1}   -1.15{col 56}{space 3}0.282{col 64}{space 4}-1.336464{col 77}{space 3} .4451139
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .6196271{col 36}{space 2} .2023626{col 47}{space 1}    3.06{col 56}{space 3}0.016{col 64}{space 4} .1529782{col 77}{space 3} 1.086276
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        20
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     18{txt}){col 67}= {res}      2.06
{txt}{col 51}Prob > F{col 67}= {res}    0.1680
{txt}{col 51}R-squared{col 67}= {res}    0.1065
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0568
{txt}{col 51}Within R-sq.{col 67}= {res}    0.1065
{txt}{col 51}Root MSE{col 67}= {res}    0.1070

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .3899019{col 36}{space 2} .2714005{col 47}{space 1}    1.44{col 56}{space 3}0.168{col 64}{space 4}-.1802893{col 77}{space 3} .9600931
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .1758216{col 36}{space 2} .1316902{col 47}{space 1}    1.34{col 56}{space 3}0.198{col 64}{space 4}-.1008493{col 77}{space 3} .4524925
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        11
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      9{txt}){col 67}= {res}      1.58
{txt}{col 51}Prob > F{col 67}= {res}    0.2409
{txt}{col 51}R-squared{col 67}= {res}    0.0843
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0175
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0843
{txt}{col 51}Root MSE{col 67}= {res}    0.0722

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .4155591{col 36}{space 2} .3309912{col 47}{space 1}    1.26{col 56}{space 3}0.241{col 64}{space 4}-.3331951{col 77}{space 3} 1.164313
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .2005858{col 36}{space 2} .1742821{col 47}{space 1}    1.15{col 56}{space 3}0.279{col 64}{space 4}-.1936677{col 77}{space 3} .5948393
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        54
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     52{txt}){col 67}= {res}      0.92
{txt}{col 51}Prob > F{col 67}= {res}    0.3422
{txt}{col 51}R-squared{col 67}= {res}    0.0219
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0031
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0219
{txt}{col 51}Root MSE{col 67}= {res}    0.1089

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1193831{col 36}{space 2} .1245385{col 47}{space 1}    0.96{col 56}{space 3}0.342{col 64}{space 4}-.1305217{col 77}{space 3} .3692879
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .3192273{col 36}{space 2} .0713648{col 47}{space 1}    4.47{col 56}{space 3}0.000{col 64}{space 4} .1760234{col 77}{space 3} .4624312
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}         8
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      6{txt}){col 67}= {res}     27.57
{txt}{col 51}Prob > F{col 67}= {res}    0.0019
{txt}{col 51}R-squared{col 67}= {res}    0.6879
{txt}{col 51}Adj R-squared{col 67}= {res}    0.6358
{txt}{col 51}Within R-sq.{col 67}= {res}    0.6879
{txt}{col 51}Root MSE{col 67}= {res}    0.0779

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .6523886{col 36}{space 2} .1242514{col 47}{space 1}    5.25{col 56}{space 3}0.002{col 64}{space 4} .3483563{col 77}{space 3} .9564209
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-.0153276{col 36}{space 2} .1035366{col 47}{space 1}   -0.15{col 56}{space 3}0.887{col 64}{space 4}-.2686724{col 77}{space 3} .2380173
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}         7
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      5{txt}){col 67}= {res}      0.51
{txt}{col 51}Prob > F{col 67}= {res}    0.5056
{txt}{col 51}R-squared{col 67}= {res}    0.0738
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.1115
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0738
{txt}{col 51}Root MSE{col 67}= {res}    0.1326

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .4078522{col 36}{space 2}   .56899{col 47}{space 1}    0.72{col 56}{space 3}0.506{col 64}{space 4}-1.054783{col 77}{space 3} 1.870487
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .2726026{col 36}{space 2} .3228063{col 47}{space 1}    0.84{col 56}{space 3}0.437{col 64}{space 4}-.5571974{col 77}{space 3} 1.102403
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        10
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      8{txt}){col 67}= {res}      0.54
{txt}{col 51}Prob > F{col 67}= {res}    0.4841
{txt}{col 51}R-squared{col 67}= {res}    0.1203
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0104
{txt}{col 51}Within R-sq.{col 67}= {res}    0.1203
{txt}{col 51}Root MSE{col 67}= {res}    0.1822

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-1.581716{col 36}{space 2} 2.155686{col 47}{space 1}   -0.73{col 56}{space 3}0.484{col 64}{space 4}-6.552735{col 77}{space 3} 3.389304
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} 1.206755{col 36}{space 2} 1.133503{col 47}{space 1}    1.06{col 56}{space 3}0.318{col 64}{space 4}-1.407108{col 77}{space 3} 3.820617
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}         8
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      6{txt}){col 67}= {res}      4.03
{txt}{col 51}Prob > F{col 67}= {res}    0.0915
{txt}{col 51}R-squared{col 67}= {res}    0.1609
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0211
{txt}{col 51}Within R-sq.{col 67}= {res}    0.1609
{txt}{col 51}Root MSE{col 67}= {res}    0.1564

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .9957437{col 36}{space 2} .4960478{col 47}{space 1}    2.01{col 56}{space 3}0.091{col 64}{space 4}-.2180416{col 77}{space 3} 2.209529
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-.0574512{col 36}{space 2} .2527885{col 47}{space 1}   -0.23{col 56}{space 3}0.828{col 64}{space 4}-.6760023{col 77}{space 3} .5610998
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        16
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     14{txt}){col 67}= {res}      0.91
{txt}{col 51}Prob > F{col 67}= {res}    0.3555
{txt}{col 51}R-squared{col 67}= {res}    0.0968
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0323
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0968
{txt}{col 51}Root MSE{col 67}= {res}    0.1401

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.6818275{col 36}{space 2} .7134614{col 47}{space 1}   -0.96{col 56}{space 3}0.355{col 64}{space 4} -2.21205{col 77}{space 3} .8483952
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .7470173{col 36}{space 2} .3871541{col 47}{space 1}    1.93{col 56}{space 3}0.074{col 64}{space 4}-.0833457{col 77}{space 3}  1.57738
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. quietly graph export figure_a11a.jpg, replace
{txt}
{com}. eststo clear 
{txt}
{com}. 
. *A-11b: Peace terms
. forvalues i = 2(1)26 {c -(}
{txt}  2{com}. eststo m1`i': reghdfe fd_pca_peacecomp0_1 fd_pca_violexposure0_1 if oblast_lag==`i', vce( robust)               
{txt}  3{com}. coefplot m*, drop(_cons) vertical yline(0, lcolor(red)) levels(95 90) legend(off) ///
> coeflabels(m12= "City of Kyiv" m13= "Kyiv oblast"  m14= "Vinnytsia" m15= "Volyn" m16= "Dnipropetrovsk" m17= "Donetsk" m18= "Zhytomyr" m19= "Zakarpattya" m110= "Zaporizhzhya" m111= "Ivano-Frankivsk" m112= "Kirovohrad" m113= "Luhansk" m114= "Lviv" m115= "Mykolayiv" m116= "Odesa" m117= "Poltava" m118= "Rivne" m119= "Sumy" m120= "Ternopil" m121 = "Kharkiv" m122= "Kherson" m123= "Khmelnytskiy" m124= "Cherkasy" m125= "Chernivtsi" m126= "Chernihiv",   labsize(small) angle(45)) aseq swapnames
{txt}  4{com}.         {c )-}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       166
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}    164{txt}){col 67}= {res}      3.85
{txt}{col 51}Prob > F{col 67}= {res}    0.0515
{txt}{col 51}R-squared{col 67}= {res}    0.0605
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0547
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0605
{txt}{col 51}Root MSE{col 67}= {res}    0.0768

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1273759{col 36}{space 2}  .064934{col 47}{space 1}    1.96{col 56}{space 3}0.051{col 64}{space 4}-.0008387{col 77}{space 3} .2555904
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5052358{col 36}{space 2} .0356935{col 47}{space 1}   14.15{col 56}{space 3}0.000{col 64}{space 4} .4347578{col 77}{space 3} .5757139
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        13
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     11{txt}){col 67}= {res}      1.10
{txt}{col 51}Prob > F{col 67}= {res}    0.3158
{txt}{col 51}R-squared{col 67}= {res}    0.0986
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0167
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0986
{txt}{col 51}Root MSE{col 67}= {res}    0.0502

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.1598374{col 36}{space 2} .1520669{col 47}{space 1}   -1.05{col 56}{space 3}0.316{col 64}{space 4}-.4945343{col 77}{space 3} .1748596
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .6500476{col 36}{space 2} .0785643{col 47}{space 1}    8.27{col 56}{space 3}0.000{col 64}{space 4} .4771288{col 77}{space 3} .8229664
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        26
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      0.72
{txt}{col 51}Prob > F{col 67}= {res}    0.4058
{txt}{col 51}R-squared{col 67}= {res}    0.0281
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0124
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0281
{txt}{col 51}Root MSE{col 67}= {res}    0.0392

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0629933{col 36}{space 2} .0744467{col 47}{space 1}   -0.85{col 56}{space 3}0.406{col 64}{space 4}-.2166438{col 77}{space 3} .0906571
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .6065003{col 36}{space 2} .0439505{col 47}{space 1}   13.80{col 56}{space 3}0.000{col 64}{space 4}  .515791{col 77}{space 3} .6972096
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        12
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     10{txt}){col 67}= {res}      0.76
{txt}{col 51}Prob > F{col 67}= {res}    0.4035
{txt}{col 51}R-squared{col 67}= {res}    0.0695
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0235
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0695
{txt}{col 51}Root MSE{col 67}= {res}    0.0273

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0825652{col 36}{space 2} .0946552{col 47}{space 1}   -0.87{col 56}{space 3}0.404{col 64}{space 4}-.2934701{col 77}{space 3} .1283398
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .6242486{col 36}{space 2} .0544695{col 47}{space 1}   11.46{col 56}{space 3}0.000{col 64}{space 4}  .502883{col 77}{space 3} .7456142
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        72
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     70{txt}){col 67}= {res}      0.02
{txt}{col 51}Prob > F{col 67}= {res}    0.8765
{txt}{col 51}R-squared{col 67}= {res}    0.0004
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0139
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0004
{txt}{col 51}Root MSE{col 67}= {res}    0.0652

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0141757{col 36}{space 2} .0909109{col 47}{space 1}    0.16{col 56}{space 3}0.877{col 64}{space 4}-.1671404{col 77}{space 3} .1954918
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}  .580251{col 36}{space 2}  .050051{col 47}{space 1}   11.59{col 56}{space 3}0.000{col 64}{space 4} .4804274{col 77}{space 3} .6800745
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        31
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     29{txt}){col 67}= {res}      0.03
{txt}{col 51}Prob > F{col 67}= {res}    0.8544
{txt}{col 51}R-squared{col 67}= {res}    0.0010
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0335
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0010
{txt}{col 51}Root MSE{col 67}= {res}    0.0725

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0239846{col 36}{space 2} .1295654{col 47}{space 1}    0.19{col 56}{space 3}0.854{col 64}{space 4}-.2410063{col 77}{space 3} .2889755
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5814201{col 36}{space 2} .0726761{col 47}{space 1}    8.00{col 56}{space 3}0.000{col 64}{space 4} .4327808{col 77}{space 3} .7300594
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        27
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     25{txt}){col 67}= {res}      0.01
{txt}{col 51}Prob > F{col 67}= {res}    0.9174
{txt}{col 51}R-squared{col 67}= {res}    0.0003
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0397
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0003
{txt}{col 51}Root MSE{col 67}= {res}    0.0622

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0095672{col 36}{space 2} .0913392{col 47}{space 1}   -0.10{col 56}{space 3}0.917{col 64}{space 4}-.1976837{col 77}{space 3} .1785494
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5643843{col 36}{space 2} .0546658{col 47}{space 1}   10.32{col 56}{space 3}0.000{col 64}{space 4}  .451798{col 77}{space 3} .6769705
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}         9
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      7{txt}){col 67}= {res}      5.45
{txt}{col 51}Prob > F{col 67}= {res}    0.0523
{txt}{col 51}R-squared{col 67}= {res}    0.4915
{txt}{col 51}Adj R-squared{col 67}= {res}    0.4188
{txt}{col 51}Within R-sq.{col 67}= {res}    0.4915
{txt}{col 51}Root MSE{col 67}= {res}    0.1179

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .6818771{col 36}{space 2}   .29221{col 47}{space 1}    2.33{col 56}{space 3}0.052{col 64}{space 4}-.0090898{col 77}{space 3} 1.372844
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .2285389{col 36}{space 2} .1702949{col 47}{space 1}    1.34{col 56}{space 3}0.221{col 64}{space 4}-.1741445{col 77}{space 3} .6312224
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        35
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     33{txt}){col 67}= {res}      1.22
{txt}{col 51}Prob > F{col 67}= {res}    0.2773
{txt}{col 51}R-squared{col 67}= {res}    0.0563
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0277
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0563
{txt}{col 51}Root MSE{col 67}= {res}    0.0772

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .2201087{col 36}{space 2} .1992472{col 47}{space 1}    1.10{col 56}{space 3}0.277{col 64}{space 4}-.1852627{col 77}{space 3} .6254801
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4702188{col 36}{space 2} .1074576{col 47}{space 1}    4.38{col 56}{space 3}0.000{col 64}{space 4} .2515947{col 77}{space 3}  .688843
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        16
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     14{txt}){col 67}= {res}      0.60
{txt}{col 51}Prob > F{col 67}= {res}    0.4529
{txt}{col 51}R-squared{col 67}= {res}    0.0076
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0633
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0076
{txt}{col 51}Root MSE{col 67}= {res}    0.0927

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0561997{col 36}{space 2} .0727967{col 47}{space 1}    0.77{col 56}{space 3}0.453{col 64}{space 4}-.0999338{col 77}{space 3} .2123331
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5629254{col 36}{space 2} .0555401{col 47}{space 1}   10.14{col 56}{space 3}0.000{col 64}{space 4} .4438037{col 77}{space 3}  .682047
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        14
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     12{txt}){col 67}= {res}      2.06
{txt}{col 51}Prob > F{col 67}= {res}    0.1766
{txt}{col 51}R-squared{col 67}= {res}    0.1271
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0543
{txt}{col 51}Within R-sq.{col 67}= {res}    0.1271
{txt}{col 51}Root MSE{col 67}= {res}    0.0357

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1779224{col 36}{space 2} .1239081{col 47}{space 1}    1.44{col 56}{space 3}0.177{col 64}{space 4}-.0920501{col 77}{space 3} .4478949
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4658543{col 36}{space 2} .0683733{col 47}{space 1}    6.81{col 56}{space 3}0.000{col 64}{space 4} .3168818{col 77}{space 3} .6148269
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        11
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      9{txt}){col 67}= {res}      0.15
{txt}{col 51}Prob > F{col 67}= {res}    0.7094
{txt}{col 51}R-squared{col 67}= {res}    0.0167
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0926
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0167
{txt}{col 51}Root MSE{col 67}= {res}    0.0855

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0780457{col 36}{space 2}  .202866{col 47}{space 1}   -0.38{col 56}{space 3}0.709{col 64}{space 4}-.5369605{col 77}{space 3} .3808691
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .6094391{col 36}{space 2} .1181311{col 47}{space 1}    5.16{col 56}{space 3}0.001{col 64}{space 4} .3422079{col 77}{space 3} .8766702
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        64
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     62{txt}){col 67}= {res}      0.02
{txt}{col 51}Prob > F{col 67}= {res}    0.8768
{txt}{col 51}R-squared{col 67}= {res}    0.0009
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0152
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0009
{txt}{col 51}Root MSE{col 67}= {res}    0.0595

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0182771{col 36}{space 2} .1174467{col 47}{space 1}    0.16{col 56}{space 3}0.877{col 64}{space 4}-.2164955{col 77}{space 3} .2530496
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5683306{col 36}{space 2}  .063896{col 47}{space 1}    8.89{col 56}{space 3}0.000{col 64}{space 4} .4406044{col 77}{space 3} .6960568
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        36
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     34{txt}){col 67}= {res}      1.93
{txt}{col 51}Prob > F{col 67}= {res}    0.1737
{txt}{col 51}R-squared{col 67}= {res}    0.0555
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0277
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0555
{txt}{col 51}Root MSE{col 67}= {res}    0.0352

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0752608{col 36}{space 2} .0541662{col 47}{space 1}   -1.39{col 56}{space 3}0.174{col 64}{space 4}-.1853398{col 77}{space 3} .0348181
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .6100755{col 36}{space 2} .0283713{col 47}{space 1}   21.50{col 56}{space 3}0.000{col 64}{space 4} .5524181{col 77}{space 3} .6677329
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        30
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     28{txt}){col 67}= {res}      0.37
{txt}{col 51}Prob > F{col 67}= {res}    0.5501
{txt}{col 51}R-squared{col 67}= {res}    0.0230
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0119
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0230
{txt}{col 51}Root MSE{col 67}= {res}    0.0785

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0978786{col 36}{space 2}  .161802{col 47}{space 1}    0.60{col 56}{space 3}0.550{col 64}{space 4}-.2335576{col 77}{space 3} .4293149
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5203485{col 36}{space 2} .0900725{col 47}{space 1}    5.78{col 56}{space 3}0.000{col 64}{space 4} .3358434{col 77}{space 3} .7048537
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        35
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     33{txt}){col 67}= {res}      0.19
{txt}{col 51}Prob > F{col 67}= {res}    0.6641
{txt}{col 51}R-squared{col 67}= {res}    0.0032
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0271
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0032
{txt}{col 51}Root MSE{col 67}= {res}    0.0900

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0785326{col 36}{space 2} .1791965{col 47}{space 1}    0.44{col 56}{space 3}0.664{col 64}{space 4}-.2860454{col 77}{space 3} .4431107
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5339117{col 36}{space 2}   .09514{col 47}{space 1}    5.61{col 56}{space 3}0.000{col 64}{space 4}  .340348{col 77}{space 3} .7274755
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        15
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     13{txt}){col 67}= {res}      0.82
{txt}{col 51}Prob > F{col 67}= {res}    0.3824
{txt}{col 51}R-squared{col 67}= {res}    0.0312
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0434
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0312
{txt}{col 51}Root MSE{col 67}= {res}    0.0817

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}  .184898{col 36}{space 2} .2045112{col 47}{space 1}    0.90{col 56}{space 3}0.382{col 64}{space 4}-.2569214{col 77}{space 3} .6267175
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4827619{col 36}{space 2} .0979247{col 47}{space 1}    4.93{col 56}{space 3}0.000{col 64}{space 4} .2712083{col 77}{space 3} .6943154
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        38
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     36{txt}){col 67}= {res}      2.33
{txt}{col 51}Prob > F{col 67}= {res}    0.1355
{txt}{col 51}R-squared{col 67}= {res}    0.1005
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0755
{txt}{col 51}Within R-sq.{col 67}= {res}    0.1005
{txt}{col 51}Root MSE{col 67}= {res}    0.0744

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .2263774{col 36}{space 2} .1482423{col 47}{space 1}    1.53{col 56}{space 3}0.135{col 64}{space 4} -.074272{col 77}{space 3} .5270267
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .4913545{col 36}{space 2} .0769138{col 47}{space 1}    6.39{col 56}{space 3}0.000{col 64}{space 4} .3353661{col 77}{space 3} .6473429
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        19
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     17{txt}){col 67}= {res}      0.01
{txt}{col 51}Prob > F{col 67}= {res}    0.9100
{txt}{col 51}R-squared{col 67}= {res}    0.0024
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0563
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0024
{txt}{col 51}Root MSE{col 67}= {res}    0.0399

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0265061{col 36}{space 2} .2311476{col 47}{space 1}   -0.11{col 56}{space 3}0.910{col 64}{space 4}-.5141849{col 77}{space 3} .4611726
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5796657{col 36}{space 2} .1298206{col 47}{space 1}    4.47{col 56}{space 3}0.000{col 64}{space 4} .3057682{col 77}{space 3} .8535633
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        75
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     73{txt}){col 67}= {res}      8.54
{txt}{col 51}Prob > F{col 67}= {res}    0.0046
{txt}{col 51}R-squared{col 67}= {res}    0.1133
{txt}{col 51}Adj R-squared{col 67}= {res}    0.1012
{txt}{col 51}Within R-sq.{col 67}= {res}    0.1133
{txt}{col 51}Root MSE{col 67}= {res}    0.0548

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1405303{col 36}{space 2}  .048096{col 47}{space 1}    2.92{col 56}{space 3}0.005{col 64}{space 4} .0446751{col 77}{space 3} .2363855
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5015637{col 36}{space 2} .0280126{col 47}{space 1}   17.90{col 56}{space 3}0.000{col 64}{space 4} .4457346{col 77}{space 3} .5573927
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        11
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      9{txt}){col 67}= {res}      0.15
{txt}{col 51}Prob > F{col 67}= {res}    0.7033
{txt}{col 51}R-squared{col 67}= {res}    0.0102
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0998
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0102
{txt}{col 51}Root MSE{col 67}= {res}    0.0312

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0211485{col 36}{space 2} .0537806{col 47}{space 1}    0.39{col 56}{space 3}0.703{col 64}{space 4}-.1005118{col 77}{space 3} .1428087
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5756986{col 36}{space 2} .0351498{col 47}{space 1}   16.38{col 56}{space 3}0.000{col 64}{space 4} .4961841{col 77}{space 3} .6552131
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        10
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      8{txt}){col 67}= {res}      0.80
{txt}{col 51}Prob > F{col 67}= {res}    0.3974
{txt}{col 51}R-squared{col 67}= {res}    0.0487
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0702
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0487
{txt}{col 51}Root MSE{col 67}= {res}    0.0145

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0432078{col 36}{space 2} .0483317{col 47}{space 1}   -0.89{col 56}{space 3}0.397{col 64}{space 4}-.1546609{col 77}{space 3} .0682454
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .6008528{col 36}{space 2} .0293058{col 47}{space 1}   20.50{col 56}{space 3}0.000{col 64}{space 4} .5332735{col 77}{space 3} .6684321
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        16
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     14{txt}){col 67}= {res}      2.33
{txt}{col 51}Prob > F{col 67}= {res}    0.1488
{txt}{col 51}R-squared{col 67}= {res}    0.3098
{txt}{col 51}Adj R-squared{col 67}= {res}    0.2605
{txt}{col 51}Within R-sq.{col 67}= {res}    0.3098
{txt}{col 51}Root MSE{col 67}= {res}    0.0530

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .4127132{col 36}{space 2} .2701393{col 47}{space 1}    1.53{col 56}{space 3}0.149{col 64}{space 4}-.1666779{col 77}{space 3} .9921043
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .3748829{col 36}{space 2} .1356757{col 47}{space 1}    2.76{col 56}{space 3}0.015{col 64}{space 4} .0838876{col 77}{space 3} .6658783
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        10
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}      8{txt}){col 67}= {res}      0.39
{txt}{col 51}Prob > F{col 67}= {res}    0.5483
{txt}{col 51}R-squared{col 67}= {res}    0.0477
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0714
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0477
{txt}{col 51}Root MSE{col 67}= {res}    0.1203

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .4115397{col 36}{space 2} .6565886{col 47}{space 1}    0.63{col 56}{space 3}0.548{col 64}{space 4}-1.102556{col 77}{space 3} 1.925636
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .3268922{col 36}{space 2} .3663261{col 47}{space 1}    0.89{col 56}{space 3}0.398{col 64}{space 4}-.5178573{col 77}{space 3} 1.171642
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}        14
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     12{txt}){col 67}= {res}      0.30
{txt}{col 51}Prob > F{col 67}= {res}    0.5939
{txt}{col 51}R-squared{col 67}= {res}    0.0098
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0727
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0098
{txt}{col 51}Root MSE{col 67}= {res}    0.0495

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0625067{col 36}{space 2} .1141228{col 47}{space 1}    0.55{col 56}{space 3}0.594{col 64}{space 4}-.1861455{col 77}{space 3} .3111589
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5446152{col 36}{space 2} .0531326{col 47}{space 1}   10.25{col 56}{space 3}0.000{col 64}{space 4} .4288492{col 77}{space 3} .6603811
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly graph export figure_a11b.jpg, replace
{txt}
{com}. eststo clear 
{txt}
{com}. 
. 
. ***FIGURE A-12: First-difference estimates of relationship between violence exposure and attitudes, online panel – jackknife
. 
. *A-12a: Negotiating principlea
. foreach v in fd_pca_principlesneg0_1 {c -(}
{txt}  2{com}.         eststo m`v': reghdfe `v' fd_pca_violexposure0_1, vce(cluster oblast_lag)
{txt}  3{com}. {c )-}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       633
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}      2.96
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0984
{txt}{col 51}R-squared{col 67}= {res}    0.0035
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0019
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0035
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1225

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0630193{col 36}{space 2} .0366498{col 47}{space 1}    1.72{col 56}{space 3}0.098{col 64}{space 4} -.012622{col 77}{space 3} .1386607
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .3620435{col 36}{space 2} .0198842{col 47}{space 1}   18.21{col 56}{space 3}0.000{col 64}{space 4} .3210045{col 77}{space 3} .4030824
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. foreach v in fd_pca_principlesneg0_1 {c -(}
{txt}  2{com}.         eststo jk`v': jackknife: reg `v' fd_pca_violexposure0_1, cluster(oblast_lag)
{txt}  3{com}. {c )-}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{text}Jackknife replications ({result:25}){text}: {res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}10{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}20{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{text} done
{res}
{txt}{col 1}Linear regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:633}
{txt}{col 57}{lalign 13:Replications}{col 70} = {res}{ralign 6:25}
{txt}{col 57}{lalign 13:F({res:1}, {res:24})}{col 70} = {res}{ralign 6:2.65}
{txt}{col 57}{lalign 13:Prob > F}{col 70} = {res}{ralign 6:0.1165}
{txt}{col 57}{lalign 13:R-squared}{col 70} = {res}{ralign 6:0.0035}
{txt}{col 57}{lalign 13:Adj R-squared}{col 70} = {res}{ralign 6:0.0019}
{txt}{col 57}{lalign 13:Root MSE}{col 70} = {res}{ralign 6:0.1225}

{txt}{ralign 88:(Replications based on {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}   Jackknife
{col 1}fd_pca_principlesneg~1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0630193{col 36}{space 2} .0387006{col 47}{space 1}    1.63{col 56}{space 3}0.117{col 64}{space 4}-.0168548{col 77}{space 3} .1428935
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .3620435{col 36}{space 2} .0209139{col 47}{space 1}   17.31{col 56}{space 3}0.000{col 64}{space 4} .3188792{col 77}{space 3} .4052077
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         
. coefplot (m*) (jk*), drop(_cons) vertical yline(0, lcolor(white)) levels(95 90) coeflabels(mfd_pca_principlesneg0_1="PCA principles of neg., FD - no jackknife" jkfd_pca_principlesneg0_1="PCA principles of neg., FD - with jackknife" ,  labsize(small) angle(45)) legend(off) aseq swapnames
{res}{txt}
{com}. 
. quietly graph export figure_a12a.jpg, replace
{txt}
{com}. 
. eststo clear 
{txt}
{com}. 
. 
. 
. *A-12b: Peace terms
. foreach v in fd_pca_peacecomp0_1 {c -(}
{txt}  2{com}.         eststo m`v': reghdfe `v' fd_pca_violexposure0_1, vce(cluster oblast_lag)
{txt}  3{com}. {c )-}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       805
{txt}Absorbing 1 HDFE group{col 51}F({res}   1{txt},{res}     24{txt}){col 67}= {res}     20.52
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0001
{txt}{col 51}R-squared{col 67}= {res}    0.0292
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0279
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0292
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.0688

{txt}{ralign 88:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1024695{col 36}{space 2} .0226232{col 47}{space 1}    4.53{col 56}{space 3}0.000{col 64}{space 4} .0557774{col 77}{space 3} .1491615
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5236406{col 36}{space 2} .0121082{col 47}{space 1}   43.25{col 56}{space 3}0.000{col 64}{space 4} .4986505{col 77}{space 3} .5486307
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. foreach v in fd_pca_peacecomp0_1 {c -(}
{txt}  2{com}.         eststo jk`v': jackknife: reg `v' fd_pca_violexposure0_1, cluster(oblast_lag)
{txt}  3{com}. {c )-}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{text}Jackknife replications ({result:25}){text}: {res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}10{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}20{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{text} done
{res}
{txt}{col 1}Linear regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:805}
{txt}{col 57}{lalign 13:Replications}{col 70} = {res}{ralign 6:25}
{txt}{col 57}{lalign 13:F({res:1}, {res:24})}{col 70} = {res}{ralign 6:16.71}
{txt}{col 57}{lalign 13:Prob > F}{col 70} = {res}{ralign 6:0.0004}
{txt}{col 57}{lalign 13:R-squared}{col 70} = {res}{ralign 6:0.0292}
{txt}{col 57}{lalign 13:Adj R-squared}{col 70} = {res}{ralign 6:0.0279}
{txt}{col 57}{lalign 13:Root MSE}{col 70} = {res}{ralign 6:0.0688}

{txt}{ralign 88:(Replications based on {res:25} clusters in {res:oblast_lag})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}   Jackknife
{col 1}   fd_pca_peacecomp0_1{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1024695{col 36}{space 2} .0250659{col 47}{space 1}    4.09{col 56}{space 3}0.000{col 64}{space 4}  .050736{col 77}{space 3} .1542029
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .5236406{col 36}{space 2} .0138397{col 47}{space 1}   37.84{col 56}{space 3}0.000{col 64}{space 4} .4950769{col 77}{space 3} .5522043
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         
. coefplot (m*) (jk*), drop(_cons) vertical yline(0, lcolor(white)) levels(95 90) coeflabels(mfd_pca_peacecomp0_1="PCA peace components - no jackknife" jkfd_pca_peacecomp0_1="PCA peace components - with jackknife" ,  labsize(small) angle(45)) legend(off) aseq swapnames
{res}{txt}
{com}. 
. quietly graph export figure_a12b.jpg, replace
{txt}
{com}. eststo clear 
{txt}
{com}. 
. 
. **********************************************************************************
. ***FIGURE A-10: Self reported exposure and recorded combat activity in region
. 
. // load in intersected, raw data
. // NOTE: qgis export excludes text features
. 
. import delimited "events_latest_14sept2022_inter.csv", clear 
{res}{txt}(encoding automatically selected: UTF-8)
{text}(91 vars, 156,379 obs)

{com}. 
.         
.         gen vina_event=1
{txt}
{com}.         tostring date, gen(date_s)
{txt}date_s generated as {res:str8}

{com}.         
.         gen smart_date=date(date_s, "YMD")
{txt}
{com}. 
.         rename adm1_pcode ADM1_PCODE
{res}{txt}
{com}.         
. gen oblast_feb24 =.
{txt}(156,379 missing values generated)

{com}. 
. // NOT PRESENT:                                                                                                                 Avtonomna Respublika Krym       UA01
. // NOT PRESENT:                                                                                                                 Sevastopilska   UA85    
. 
. //       4. Vinnytsia oblast |        138        4.58       22.94,              Vinnytska       UA05
.         replace oblast_feb24=4 if ADM1_PCODE=="UA05"
{txt}(1,209 real changes made)

{com}.         
. //           5. Volyn oblast |         68        2.25       25.20,              Volynska        UA07
.         replace oblast_feb24=5 if ADM1_PCODE=="UA07"
{txt}(808 real changes made)

{com}.         
. //  6. Dnipropetrovsk oblast |        324       10.74       35.94,              Dnipropetrovska UA12
.         replace oblast_feb24=6  if ADM1_PCODE=="UA12" 
{txt}(5,615 real changes made)

{com}.         
. //         7. Donetsk oblast |        115        3.81       39.75,              Donetska        UA14
.         replace oblast_feb24=7 if ADM1_PCODE=="UA14"  
{txt}(30,608 real changes made)

{com}. 
. //        8. Zhytomyr oblast |         83        2.75       42.51,              Zhytomyrska     UA18
.         replace oblast_feb24=8 if ADM1_PCODE=="UA18"  
{txt}(1,745 real changes made)

{com}.         
. //     9. Zakarpattia oblast |         36        1.19       43.70,              Zakarpatska     UA21
.         replace oblast_feb24=9 if ADM1_PCODE=="UA21" 
{txt}(688 real changes made)

{com}.         
. //   10. Zaporizhzhia oblast |        123        4.08       47.78,              Zaporizka       UA23
.         replace oblast_feb24=10 if ADM1_PCODE=="UA23"  
{txt}(12,484 real changes made)

{com}.         
. //11. Ivano-Frankivsk oblast |         63        2.09       49.87,              Ivano-Frankivska        UA26
.         replace oblast_feb24=11 if ADM1_PCODE=="UA26"  
{txt}(292 real changes made)

{com}.         
. //            3. Kyiv oblast |        191        6.33       18.37,              Kyivska UA32
.         replace oblast_feb24=3 if ADM1_PCODE=="UA32" 
{txt}(10,051 real changes made)

{com}.         
. //     12. Kirovohrad oblast |         65        2.16       52.02,              Kirovohradska   UA35
.         replace oblast_feb24=12 if ADM1_PCODE=="UA35" 
{txt}(396 real changes made)

{com}.         
. //       13. Luhans'k oblast |         28        0.93       52.95,              Luhanska        UA44
.         replace oblast_feb24=13 if ADM1_PCODE=="UA44" 
{txt}(10,789 real changes made)

{com}.         
. //           14. Lviv oblast |        159        5.27       58.22,              Lvivska UA46
.         replace oblast_feb24=14 if ADM1_PCODE=="UA46"  
{txt}(3,665 real changes made)

{com}.         
. //       15. Mykolaiv oblast |        106        3.51       61.74,              Mykolaivska     UA48
.         replace oblast_feb24=15 if ADM1_PCODE=="UA48"  
{txt}(7,001 real changes made)

{com}.         
. //          16. Odesa oblast |        211        7.00       68.73,              Odeska  UA51
.         replace oblast_feb24=16 if ADM1_PCODE=="UA51" 
{txt}(7,050 real changes made)

{com}.         
. //        17. Poltava oblast |        103        3.42       72.15,              Poltavska       UA53
.         replace oblast_feb24=17 if ADM1_PCODE=="UA53"  
{txt}(1,012 real changes made)

{com}.         
. //          18. Rivne oblast |         76        2.52       74.67,              Rivnenska       UA56
.         replace oblast_feb24=18 if ADM1_PCODE=="UA56"  
{txt}(835 real changes made)

{com}.         
. //           19. Sumy oblast |        100        3.32       77.98,              Sumska  UA59
.         replace oblast_feb24=19 if ADM1_PCODE=="UA59" 
{txt}(4,819 real changes made)

{com}.         
. //       20. Ternopil oblast |         48        1.59       79.58,              Ternopilska     UA61
.         replace oblast_feb24=20 if ADM1_PCODE=="UA61"  
{txt}(676 real changes made)

{com}.         
. //        21. Kharkiv oblast |        249        8.26       87.83,              Kharkivska      UA63
.         replace oblast_feb24=21 if ADM1_PCODE=="UA63"  
{txt}(17,651 real changes made)

{com}.         
. //        22. Kherson oblast |         22        0.73       88.56,              Khersonska      UA65
.         replace oblast_feb24=22 if ADM1_PCODE=="UA65"  
{txt}(10,427 real changes made)

{com}.         
. //   23. Khmelnytskyi oblast |         83        2.75       91.31,              Khmelnytska     UA68
.         replace oblast_feb24=23 if ADM1_PCODE=="UA68"  
{txt}(660 real changes made)

{com}.         
. //       24. Cherkasy oblast |        100        3.32       94.63,              Cherkaska       UA71
.         replace oblast_feb24=24 if ADM1_PCODE=="UA71" 
{txt}(460 real changes made)

{com}.         
. //     25. Chernivtsi oblast |         54        1.79       96.42,              Chernivetska    UA73
.         replace oblast_feb24=25 if ADM1_PCODE=="UA73" 
{txt}(212 real changes made)

{com}.         
. //      26. Chernihiv oblast |        108        3.58      100.00,              Chernihivska    UA74
.         replace oblast_feb24=26 if ADM1_PCODE=="UA74"  
{txt}(3,672 real changes made)

{com}.         
. //           2. City of Kyiv |        363       12.04       12.04,              Kyivska CITY UA80
.         replace oblast_feb24=2 if ADM1_PCODE=="UA80"    
{txt}(18,051 real changes made)

{com}.         
. collapse (sum) vina_event t_aad_b t_airstrike_b t_armor_b t_arrest_b t_artillery_b t_control_b t_killing_b t_firefight_b t_ied_b t_property_b t_raid_b t_occupy_b t_cyber_b t_hospital_b t_milcas_b t_civcas_b, by(oblast_feb24 smart_date)
{res}{txt}
{com}. 
. tsset oblast_feb24 smart_date
{res}
{col 1}{txt:Panel variable: }{res:oblast_feb24}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:smart_date}{txt:, }{res:{bind:22700}}{txt: to }{res:{bind:22902}}{txt:, but with gaps}{p_end}
{txt}{col 10}Delta: {res}1 unit
{txt}
{com}. 
.         tsfill, full 
{txt}
{com}. 
.         replace vina_event=0 if vina_event==. 
{txt}(682 real changes made)

{com}. 
.         
. tsset oblast_feb24 smart_date
{res}
{col 1}{txt:Panel variable: }{res:oblast_feb24}{txt: (weakly balanced)}
{p 1 16 2}{txt:Time variable: }{res:smart_date}{txt:, }{res:{bind:22700}}{txt: to }{res:{bind:22902}}{p_end}
{txt}{col 10}Delta: {res}1 unit
{txt}
{com}. 
.         gen prior_week_events= vina_event + L.vina_event + L2.vina_event + L3.vina_event + L4.vina_event + L5.vina_event + L6.vina_event
{txt}(156 missing values generated)

{com}. 
. foreach v in t_aad_b t_airstrike_b t_armor_b t_arrest_b t_artillery_b t_control_b t_killing_b t_firefight_b t_ied_b t_property_b t_raid_b t_occupy_b t_cyber_b t_hospital_b t_milcas_b t_civcas_b{c -(}
{txt}  2{com}.         
.         replace `v'=0 if `v'==. 
{txt}  3{com}.         gen prior_week_`v'=`v'+L.`v' + L2.`v' + L3.`v' + L4.`v' + L5.`v' + L6.`v'
{txt}  4{com}. {c )-}
{txt}(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)
(682 real changes made)
(156 missing values generated)

{com}.                 
.         rename smart_date smart_start_date 
{res}{txt}
{com}.         
.         sort oblast_feb24 smart_start_date
{txt}
{com}.         
. save "vina_oblastXday.dta", replace
{txt}{p 0 4 2}
file {bf}
vina_oblastXday.dta{rm}
saved
{p_end}

{com}.         
. clear all
{res}{txt}
{com}. 
. 
. 
. 
. 
. use "RedLines_dataset.dta", clear
{txt}
{com}. 
. keep if wave==1&telephone==1
{txt}(5,471 observations deleted)

{com}. 
. tostring start_day start_month start_year, force replace
{txt}start_day was {res:double} now {res:str2}
start_month was {res:double} now {res:str1}
start_year was {res:double} now {res:str4}

{com}. 
.         gen start_date= start_day + "/" + start_month + "/" + start_year
{txt}
{com}. 
.         gen smart_start_date=date(start_date, "DMY")
{txt}
{com}.         
.         sort oblast_feb24 smart_start_date 
{txt}
{com}.         
.     merge oblast_feb24 smart_start_date using "vina_oblastXday.dta"
{txt}{p}
(you are using old
{bf:merge} syntax; see
{bf:{help merge:[D] merge}} for new syntax)
{p_end}
{p 0 4 2}
variable{txt}s{txt} oblast_feb24
smart_start_date
do not uniquely identify observations in
the master data
{p_end}

{com}. 
.         keep if _merge==3
{txt}(4,987 observations deleted)

{com}.         
.         
.         foreach v in prior_week_events prior_week_t_aad_b prior_week_t_airstrike_b prior_week_t_armor_b prior_week_t_arrest_b prior_week_t_artillery_b prior_week_t_control_b prior_week_t_killing_b prior_week_t_firefight_b prior_week_t_ied_b prior_week_t_property_b prior_week_t_raid_b prior_week_t_occupy_b prior_week_t_cyber_b prior_week_t_hospital_b prior_week_t_milcas_b prior_week_t_civcas_b{c -(}
{txt}  2{com}.                 
.         summarize `v'
{txt}  3{com}.                 scalar `v'_mean = r(mean)       
{txt}  4{com}.                 scalar `v'_sd = r(sd)   
{txt}  5{com}.                 
.                 gen `v'_std= (`v'-`v'_mean)/(`v'_sd)
{txt}  6{com}.         {c )-}

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_week~s {c |}{res}      3,016    220.7968    216.8185          0        976

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~ad_b {c |}{res}      3,016    3.300729    5.052961          0         26

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_we~e_b {c |}{res}      3,016    6.109748    7.675566          0         37

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~or_b {c |}{res}      3,016     1.06996    1.789433          0          9

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~st_b {c |}{res}      3,016    3.734085    5.014365          0         22

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~ry_b {c |}{res}      3,016      47.562    73.33144          0        396

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~ol_b {c |}{res}      3,016    2.721817    3.971106          0         23

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_we~g_b {c |}{res}      3,016    .5179045    .7865016          0          5

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~ht_b {c |}{res}      3,016    1.480769    2.533679          0         18

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~ed_b {c |}{res}      3,016     2.09118    3.824765          0         19

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~ty_b {c |}{res}      3,016    8.532493    8.813769          0         40

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~id_b {c |}{res}      3,016    1.880637    2.300265          0         13

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~py_b {c |}{res}      3,016    1.836538    2.739213          0         15

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~er_b {c |}{res}      3,016    2.172745    2.648007          0         11

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~al_b {c |}{res}      3,016    1.647215     2.54659          0         13

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior~lcas_b {c |}{res}      3,016    4.954244      6.5487          0         32

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior~vcas_b {c |}{res}      3,016    16.37964    23.68991          0        117
{txt}
{com}. 
.         
. 
. 
. pca prior_week_events_std prior_week_t_aad_b_std prior_week_t_airstrike_b_std prior_week_t_armor_b_std prior_week_t_arrest_b_std prior_week_t_artillery_b_std prior_week_t_control_b_std prior_week_t_killing_b_std prior_week_t_firefight_b_std prior_week_t_ied_b_std prior_week_t_property_b_std prior_week_t_raid_b_std prior_week_t_occupy_b_std prior_week_t_cyber_b_std prior_week_t_hospital_b_std prior_week_t_milcas_b_std prior_week_t_civcas_b_std, comp(1) 

{txt}Principal components/correlation{col 50}Number of obs    = {res}     3,016
{col 50}{txt}Number of comp.  = {res}         1
{col 50}{txt}Trace            {col 67}=  {res}       17
{col 5}{txt}Rotation: (unrotated = principal){col 50}Rho              = {res}    0.5558

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}   Component {c |}   Eigenvalue   Difference         Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 12:Comp1} {c |}{res}       9.4486      7.96858             0.5558       0.5558
{txt}{col 5}{ralign 12:Comp2} {c |}{res}      1.48001      .103386             0.0871       0.6429
{txt}{col 5}{ralign 12:Comp3} {c |}{res}      1.37663      .171635             0.0810       0.7238
{txt}{col 5}{ralign 12:Comp4} {c |}{res}      1.20499      .288207             0.0709       0.7947
{txt}{col 5}{ralign 12:Comp5} {c |}{res}      .916785      .269797             0.0539       0.8486
{txt}{col 5}{ralign 12:Comp6} {c |}{res}      .646988     .0989253             0.0381       0.8867
{txt}{col 5}{ralign 12:Comp7} {c |}{res}      .548062      .182082             0.0322       0.9189
{txt}{col 5}{ralign 12:Comp8} {c |}{res}       .36598     .0983489             0.0215       0.9405
{txt}{col 5}{ralign 12:Comp9} {c |}{res}      .267631     .0813935             0.0157       0.9562
{txt}{col 5}{ralign 12:Comp10} {c |}{res}      .186237     .0140578             0.0110       0.9672
{txt}{col 5}{ralign 12:Comp11} {c |}{res}       .17218      .047959             0.0101       0.9773
{txt}{col 5}{ralign 12:Comp12} {c |}{res}      .124221     .0495669             0.0073       0.9846
{txt}{col 5}{ralign 12:Comp13} {c |}{res}     .0746537    .00921187             0.0044       0.9890
{txt}{col 5}{ralign 12:Comp14} {c |}{res}     .0654419     .0119016             0.0038       0.9928
{txt}{col 5}{ralign 12:Comp15} {c |}{res}     .0535402     .0108049             0.0031       0.9960
{txt}{col 5}{ralign 12:Comp16} {c |}{res}     .0427353     .0174165             0.0025       0.9985
{txt}{col 5}{ralign 12:Comp17} {c |}{res}     .0253188            .             0.0015       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}

Principal components (eigenvectors) 

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 13}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Comp1}{space 1}{c |}{space 1}{ralign 11:Unexplained}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 13}
{space 4}{space 0}{ralign 12:prior_~s_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3057}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .1169}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ad_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2194}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .5454}}}{space 1}
{space 4}{space 0}{ralign 12:prio~e_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2738}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .2915}}}{space 1}
{space 4}{space 0}{ralign 12:pri~or_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0723}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .9506}}}{space 1}
{space 4}{space 0}{ralign 12:pri~st_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2897}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .2072}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ry_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2827}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .2447}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ol_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2228}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .5309}}}{space 1}
{space 4}{space 0}{ralign 12:prio~g_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1069}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .8921}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ht_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1889}}}{space 1}{c |}{space 1}{center 11:{res:{sf:       .663}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ed_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2084}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .5896}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ty_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2930}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .1886}}}{space 1}
{space 4}{space 0}{ralign 12:pri~id_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2500}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .4096}}}{space 1}
{space 4}{space 0}{ralign 12:pri~py_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2219}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .5348}}}{space 1}
{space 4}{space 0}{ralign 12:pri~er_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2626}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .3485}}}{space 1}
{space 4}{space 0}{ralign 12:pri~al_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2353}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .4768}}}{space 1}
{space 4}{space 0}{ralign 12:p~lcas_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2722}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .2997}}}{space 1}
{space 4}{space 0}{ralign 12:p~vcas_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2796}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .2615}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 13}

{com}.                 predict pc1
{txt}({bf:score} assumed)

Scoring coefficients 
{col 5}sum of squares(column-loading) = 1

{space 4}{hline 13}{c  TT}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Comp1}{space 1}
{space 4}{hline 13}{c   +}{hline 10}
{space 4}{space 0}{ralign 12:prior_~s_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3057}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ad_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2194}}}{space 1}
{space 4}{space 0}{ralign 12:prio~e_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2738}}}{space 1}
{space 4}{space 0}{ralign 12:pri~or_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0723}}}{space 1}
{space 4}{space 0}{ralign 12:pri~st_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2897}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ry_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2827}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ol_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2228}}}{space 1}
{space 4}{space 0}{ralign 12:prio~g_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1069}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ht_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1889}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ed_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2084}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ty_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2930}}}{space 1}
{space 4}{space 0}{ralign 12:pri~id_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2500}}}{space 1}
{space 4}{space 0}{ralign 12:pri~py_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2219}}}{space 1}
{space 4}{space 0}{ralign 12:pri~er_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2626}}}{space 1}
{space 4}{space 0}{ralign 12:pri~al_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2353}}}{space 1}
{space 4}{space 0}{ralign 12:p~lcas_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2722}}}{space 1}
{space 4}{space 0}{ralign 12:p~vcas_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2796}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}

{com}.                 rename pc1 pca_priorweek_attacks
{res}{txt}
{com}. 
. 
. ***FIGURE A-10a: Telephone sample
. 
. tw (lpolyci pca_priorweek_attacks pca_violexposure), legend(off) xtitle("Self reported exposure (PCA), telephone sample") ytitle("Conflict events (PCA), telephone sample")
{res}{txt}
{com}. 
. quietly graph export figure_a10a.jpg, replace
{txt}
{com}.         
. *~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~*
. * ONLINE SURVEYS                                                                            *
. *~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~*
. 
. use "RedLines_dataset.dta", clear
{txt}
{com}. 
. keep if wave==1&online==1 | wave==2 & online==1
{txt}(5,029 observations deleted)

{com}.         
. tostring start_day start_month start_year, force replace
{txt}start_day was {res:double} now {res:str2}
start_month was {res:double} now {res:str1}
start_year was {res:double} now {res:str4}

{com}. 
.         gen start_date= start_day + "/" + start_month + "/" + start_year
{txt}
{com}. 
.         gen smart_start_date=date(start_date, "DMY")
{txt}
{com}.         
.         sort oblast_feb24 smart_start_date 
{txt}
{com}.         
.     merge oblast_feb24 smart_start_date using "vina_oblastXday.dta"
{txt}{p}
(you are using old
{bf:merge} syntax; see
{bf:{help merge:[D] merge}} for new syntax)
{p_end}
{p 0 4 2}
variable{txt}s{txt} oblast_feb24
smart_start_date
do not uniquely identify observations in
the master data
{p_end}

{com}. 
.         keep if _merge==3
{txt}(5,136 observations deleted)

{com}.         
. 
.         foreach v in prior_week_events prior_week_t_aad_b prior_week_t_airstrike_b prior_week_t_armor_b prior_week_t_arrest_b prior_week_t_artillery_b prior_week_t_control_b prior_week_t_killing_b prior_week_t_firefight_b prior_week_t_ied_b prior_week_t_property_b prior_week_t_raid_b prior_week_t_occupy_b prior_week_t_cyber_b prior_week_t_hospital_b prior_week_t_milcas_b prior_week_t_civcas_b{c -(}
{txt}  2{com}.                 
.         summarize `v'
{txt}  3{com}.                 scalar `v'_mean = r(mean)       
{txt}  4{com}.                 scalar `v'_sd = r(sd)   
{txt}  5{com}.                 
.                 gen `v'_std= (`v'-`v'_mean)/(`v'_sd)
{txt}  6{com}.         {c )-}

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_week~s {c |}{res}      3,458    213.4387    174.6517          0        960

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~ad_b {c |}{res}      3,458    1.935223    4.351343          0         26

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_we~e_b {c |}{res}      3,458    4.169462    6.565015          0         37

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~or_b {c |}{res}      3,458    .5780798    1.463016          0          9

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~st_b {c |}{res}      3,458    4.650376    4.005648          0         22

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~ry_b {c |}{res}      3,458    37.60006    61.04501          0        392

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~ol_b {c |}{res}      3,458    1.984384    3.840814          0         25

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_we~g_b {c |}{res}      3,458    .2770388    .6645268          0          3

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~ht_b {c |}{res}      3,458    1.643146    1.827033          0         18

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~ed_b {c |}{res}      3,458    1.151533    2.756148          0         16

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~ty_b {c |}{res}      3,458      7.3155    7.046455          0         40

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~id_b {c |}{res}      3,458    4.206189    2.698203          0         11

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~py_b {c |}{res}      3,458     2.76605    2.194329          0         13

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~er_b {c |}{res}      3,458    5.545691    3.489862          0         11

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior_w~al_b {c |}{res}      3,458    1.209659    2.225435          0         12

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior~lcas_b {c |}{res}      3,458    3.286293    5.560059          0         27

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
prior~vcas_b {c |}{res}      3,458    13.20937    18.34956          0        108
{txt}
{com}.                 
. pca prior_week_events_std prior_week_t_aad_b_std prior_week_t_airstrike_b_std prior_week_t_armor_b_std prior_week_t_arrest_b_std prior_week_t_artillery_b_std prior_week_t_control_b_std prior_week_t_killing_b_std prior_week_t_firefight_b_std prior_week_t_ied_b_std prior_week_t_property_b_std prior_week_t_raid_b_std prior_week_t_occupy_b_std prior_week_t_cyber_b_std prior_week_t_hospital_b_std prior_week_t_milcas_b_std prior_week_t_civcas_b_std, comp(1) 

{txt}Principal components/correlation{col 50}Number of obs    = {res}     3,458
{col 50}{txt}Number of comp.  = {res}         1
{col 50}{txt}Trace            {col 67}=  {res}       17
{col 5}{txt}Rotation: (unrotated = principal){col 50}Rho              = {res}    0.4629

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}   Component {c |}   Eigenvalue   Difference         Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 12:Comp1} {c |}{res}      7.86907      4.72156             0.4629       0.4629
{txt}{col 5}{ralign 12:Comp2} {c |}{res}      3.14751      1.74686             0.1851       0.6480
{txt}{col 5}{ralign 12:Comp3} {c |}{res}      1.40064      .132064             0.0824       0.7304
{txt}{col 5}{ralign 12:Comp4} {c |}{res}      1.26858      .216787             0.0746       0.8050
{txt}{col 5}{ralign 12:Comp5} {c |}{res}      1.05179      .376441             0.0619       0.8669
{txt}{col 5}{ralign 12:Comp6} {c |}{res}      .675352      .149702             0.0397       0.9066
{txt}{col 5}{ralign 12:Comp7} {c |}{res}      .525649      .137035             0.0309       0.9376
{txt}{col 5}{ralign 12:Comp8} {c |}{res}      .388615       .18481             0.0229       0.9604
{txt}{col 5}{ralign 12:Comp9} {c |}{res}      .203804     .0586368             0.0120       0.9724
{txt}{col 5}{ralign 12:Comp10} {c |}{res}      .145168     .0466887             0.0085       0.9810
{txt}{col 5}{ralign 12:Comp11} {c |}{res}     .0984788     .0348991             0.0058       0.9867
{txt}{col 5}{ralign 12:Comp12} {c |}{res}     .0635797     .0160648             0.0037       0.9905
{txt}{col 5}{ralign 12:Comp13} {c |}{res}     .0475149    .00384459             0.0028       0.9933
{txt}{col 5}{ralign 12:Comp14} {c |}{res}     .0436703     .0158132             0.0026       0.9958
{txt}{col 5}{ralign 12:Comp15} {c |}{res}     .0278571    .00444486             0.0016       0.9975
{txt}{col 5}{ralign 12:Comp16} {c |}{res}     .0234123    .00410778             0.0014       0.9989
{txt}{col 5}{ralign 12:Comp17} {c |}{res}     .0193045            .             0.0011       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}

Principal components (eigenvectors) 

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 13}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Comp1}{space 1}{c |}{space 1}{ralign 11:Unexplained}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 13}
{space 4}{space 0}{ralign 12:prior_~s_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3212}}}{space 1}{c |}{space 1}{center 11:{res:{sf:       .188}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ad_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2646}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .4491}}}{space 1}
{space 4}{space 0}{ralign 12:prio~e_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3041}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .2724}}}{space 1}
{space 4}{space 0}{ralign 12:pri~or_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1537}}}{space 1}{c |}{space 1}{center 11:{res:{sf:       .814}}}{space 1}
{space 4}{space 0}{ralign 12:pri~st_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2589}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .4727}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ry_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3070}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .2582}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ol_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2345}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .5671}}}{space 1}
{space 4}{space 0}{ralign 12:prio~g_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1888}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .7195}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ht_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1429}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .8393}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ed_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2410}}}{space 1}{c |}{space 1}{center 11:{res:{sf:       .543}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ty_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3273}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .1572}}}{space 1}
{space 4}{space 0}{ralign 12:pri~id_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0536}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .9774}}}{space 1}
{space 4}{space 0}{ralign 12:pri~py_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0804}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .9491}}}{space 1}
{space 4}{space 0}{ralign 12:pri~er_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0104}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .9992}}}{space 1}
{space 4}{space 0}{ralign 12:pri~al_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2662}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .4422}}}{space 1}
{space 4}{space 0}{ralign 12:p~lcas_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3079}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .2539}}}{space 1}
{space 4}{space 0}{ralign 12:p~vcas_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3131}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .2288}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 13}

{com}.                 predict pc1
{txt}({bf:score} assumed)

Scoring coefficients 
{col 5}sum of squares(column-loading) = 1

{space 4}{hline 13}{c  TT}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Comp1}{space 1}
{space 4}{hline 13}{c   +}{hline 10}
{space 4}{space 0}{ralign 12:prior_~s_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3212}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ad_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2646}}}{space 1}
{space 4}{space 0}{ralign 12:prio~e_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3041}}}{space 1}
{space 4}{space 0}{ralign 12:pri~or_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1537}}}{space 1}
{space 4}{space 0}{ralign 12:pri~st_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2589}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ry_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3070}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ol_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2345}}}{space 1}
{space 4}{space 0}{ralign 12:prio~g_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1888}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ht_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1429}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ed_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2410}}}{space 1}
{space 4}{space 0}{ralign 12:pri~ty_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3273}}}{space 1}
{space 4}{space 0}{ralign 12:pri~id_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0536}}}{space 1}
{space 4}{space 0}{ralign 12:pri~py_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0804}}}{space 1}
{space 4}{space 0}{ralign 12:pri~er_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0104}}}{space 1}
{space 4}{space 0}{ralign 12:pri~al_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2662}}}{space 1}
{space 4}{space 0}{ralign 12:p~lcas_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3079}}}{space 1}
{space 4}{space 0}{ralign 12:p~vcas_b_std}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3131}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}

{com}.                 rename pc1 pca_priorweek_attacks
{res}{txt}
{com}. 
. ***FIGURE A-10b: Online sample
. tw (lpolyci pca_priorweek_attacks pca_violexposure), legend(off) xtitle("Self reported exposure (PCA), online panel") ytitle("Conflict events (PCA), online panel")
{res}{txt}
{com}. 
. quietly graph export figure_a10b.jpg, replace
{txt}
{com}. 
. ************************************** FIGURES A1-A3, A5-A7 *******************************************
. **** FIGURE A1
. 
. use "RedLines_dataset.dta", clear
{txt}
{com}. 
. 
. hist age if telephone==1 & wave==1, discrete percent xlabel(20(5)90) title("Age") subtitle("(CATI Panel)") xtitle("") color(maroon) note("Number of Respondents: 3016") ///
> plotregion(margin(0 1 1 1)) 
{txt}(start={res}18{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a1_age.jpg, replace
{txt}
{com}. 
. hist gender if telephone==1 & wave==1, discrete percent gap(40) title("Gender") subtitle("(CATI Panel)") xtitle("") xlabel(0 "Male" 1 "Female") color(maroon) note("Number of Respondents: 3016") plotregion(margin(0 20 1 1)) 
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a1_gender.jpg, replace
{txt}
{com}. 
. hist education2 if telephone==1 & wave==1, discrete percent gap(20) start(1) title("Education") subtitle("(CATI Panel)") color(maroon) ///
> xlabel(1 "Primary" 2 "Secondary" 3 "Higher Education") xtitle("") ///
> note("Number of Respondents: 3016") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a1_education.jpg, replace
{txt}
{com}. 
. hist language2 if telephone==1 & wave==1, discrete percent gap(70) start(1) title("Language") subtitle("(CATI Panel)") color(maroon) ///
> xlabel(1 `""Only" "Ukrainian""' 2 `""Mostly" "Ukrainian""' 3 `""Ukrainian/" "Russian""' 4 `""Mostly" "Russian""' 5 `""Only" "Russian""' 6 "Other" 7 "Refuse") xtitle("") ///
> note("Number of Respondents: 3016") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a1_language.jpg, replace
{txt}
{com}. 
. hist religion if telephone==1 & wave==1, discrete percent gap(70) title("Religion") subtitle("(CATI Panel)") color(maroon)  xlabel(1/12, valuelabel angle(45)) xtitle("") note("Number of Respondents: 3016") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a1_religion.jpg, replace
{txt}
{com}. 
. hist stillcommunicate if telephone==1 & wave==1, discrete percent gap(70) title("Still Communicate with Relatives/Friends in Russia") subtitle("(CATI Panel)") color(maroon)  xlabel(0/1, valuelabel) xtitle("") note("Number of Respondents: 3016") plotregion(margin(0 10 1 1)) 
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a1_communicate.jpg, replace
{txt}
{com}. 
. hist settlement_type_feb24 if telephone==1 & wave==1, discrete percent gap(20)  title("Settlement Type") subtitle("(CATI Panel)") color(maroon)  xlabel(1/7, valuelabel angle(45)) xtitle("") note("Number of Respondents: 3016") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a1_settlement.jpg, replace
{txt}
{com}. 
. hist oblast_feb24 if telephone==1 & wave==1, discrete percent gap(20) start(2) title("Oblast") subtitle("(CATI Panel)") color(maroon)  xlabel(2/26, valuelabel angle(45)) xtitle("") note("Number of Respondents: 3016") plotregion(margin(0 10 1 1)) 
{txt}(start={res}2{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a1_oblast.jpg, replace
{txt}
{com}. 
. **** FIGURE A2
. 
. hist age if telephone==1 & wave==2, discrete percent xlabel(20(20)100) title("Age") subtitle("(Phone Sample 2)") xtitle("") color(maroon) note("Number of Respondents: 2013") plotregion(margin(0 1 1 1)) 
{txt}(start={res}18{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a2_age.jpg, replace
{txt}
{com}. 
. hist gender if telephone==1 & wave==2, discrete percent gap(40) title("Gender") subtitle("(Phone Sample 2)") xtitle("") xlabel(0 "Male" 1 "Female") color(maroon) note("Number of Respondents: 2013") ///
> plotregion(margin(0 20 1 1)) 
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a2_gender.jpg, replace
{txt}
{com}. 
. hist education2 if telephone==1 & wave==2, discrete percent gap(20) start(1) title("Education") subtitle("(Phone Sample 2)") color(maroon) ///
> xlabel(1 "Primary" 2 "Secondary" 3 "Higher Education") xtitle("") ///
> note("Number of Respondents: 2013") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a2_education.jpg, replace
{txt}
{com}. 
. hist language3, discrete percent gap(70) start(1) title("Language") subtitle("(Phone Sample 2)") color(maroon) ///
> xlabel(1 "Ukrainian" 2 "Russian" 3 `""Both Ukrainian" "and Russian""' 4 "Other" 5 "DK/Refuse") xtitle("") ///
> note("Number of Respondents: 2013") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a2_language.jpg, replace
{txt}
{com}. 
. hist settlement_type_feb24 if telephone==1 & wave==2, discrete percent gap(20)  title("Settlement Type") subtitle("(Phone Sample 2)") color(maroon)  xlabel(1/7, valuelabel angle(45)) xtitle("") note("Number of Respondents: 2013") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a2_settlement.jpg, replace
{txt}
{com}. 
. hist oblast_feb24 if telephone==1 & wave==2, discrete percent gap(20) start(2) title("Oblast") subtitle("(Phone Sample 2)") color(maroon)  xlabel(2/26, valuelabel angle(45)) xtitle("") note("Number of Respondents: 2013") plotregion(margin(0 10 1 1)) 
{txt}(start={res}2{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a2_oblast.jpg, replace
{txt}
{com}. 
. 
. ******* FIGURE A3
. 
. hist age if online==1, discrete percent xlabel(20(5)55) title("Age") subtitle("(Online Panel)") xtitle("") color(maroon) note("Number of Respondents: 1729") plotregion(margin(0 1 1 1)) 
{txt}(start={res}18{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a3_age.jpg, replace
{txt}
{com}. 
. hist gender if online==1, discrete percent gap(40) title("Gender") subtitle("(Online Panel)") xtitle("") xlabel(0 "Male" 1 "Female") color(maroon) note("Number of Respondents: 1729") ///
> plotregion(margin(0 20 1 1)) 
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a3_gender.jpg, replace
{txt}
{com}. 
. hist education2 if online==1, discrete percent gap(20) start(1) title("Education") subtitle("(Online Panel)") color(maroon) ///
> xlabel(1 "Primary" 2 "Secondary" 3 "Higher Education") xtitle("") ///
> note("Number of Respondents: 1729") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a3_education.jpg, replace
{txt}
{com}. 
. hist language2 if online==1, discrete percent gap(70) start(1) title("Language") subtitle("(Online Panel)") color(maroon) ///
> xlabel(1 `""Only" "Ukrainian""' 2 `""Mostly" "Ukrainian""' 3 `""Ukrainian/" "Russian""' 4 `""Mostly" "Russian""' 5 `""Only" "Russian""' 6 "Other" 7 "Refuse") xtitle("") ///
> note("Number of Respondents: 1729") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a3_language.jpg, replace
{txt}
{com}. 
. hist religion if online==1, discrete percent gap(70) title("Religion") subtitle("(Online Panel)") color(maroon)  xlabel(1/12, valuelabel angle(45)) xtitle("") note("Number of Respondents: 1729") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a3_religion.jpg, replace
{txt}
{com}. 
. hist stillcommunicate if online==1, discrete percent gap(70) title("Still Communicate with Relatives/Friends in Russia") subtitle("(Online Panel)") color(maroon)  xlabel(0/1, valuelabel) xtitle("") note("Number of Respondents: 1729") plotregion(margin(0 10 1 1)) 
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a3_communicate.jpg, replace
{txt}
{com}. 
. hist type_online_feb24 if online==1, discrete percent gap(20)  title("Settlement Type") subtitle("(Online Panel)") color(maroon)  xlabel(1/2, valuelabel) xtitle("") note("Number of Respondents: 1729") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a3_settlement.jpg, replace
{txt}
{com}. 
. hist oblast_feb24 if online==1, discrete percent gap(20) start(2) title("Oblast") subtitle("(Online Panel)") color(maroon)  xlabel(2/26, valuelabel angle(45)) xtitle("") note("Number of Respondents: 1729") plotregion(margin(0 10 1 1)) 
{txt}(start={res}2{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a3_oblast.jpg, replace
{txt}
{com}. 
. 
. ******* FIGURE A5
. 
. hist exposure_q14a if telephone==1 & wave==1, discrete percent gap(20) title("Displacement") subtitle("(CATI Panel)") color(maroon)  xlabel(1/3, valuelabel) xtitle("") note("Number of Respondents: 3016") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a5_displacement.jpg, replace
{txt}
{com}. 
. hist exposure_q14b if telephone==1 & wave==1, discrete percent gap(50) title("Did you leave ...?") subtitle("(CATI Panel)") color(maroon)  xlabel(1 `""as a result of" "the violence""' 2 `""in anticipation" "of violence""' 3 "Refuse") xtitle("") note("Number of Respondents: 1096") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a5_didyouleave.jpg, replace
{txt}
{com}. 
. hist exposure_q15a1 if telephone==1 & wave==1, discrete percent gap(50) title("Exposure to Shelling") subtitle(`"CATI Panel"' `"(n=3016)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("") note("Number of Respondents: 3016") plotregion(margin(0 10 1 1)) name(g1, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a5_shelling.jpg, replace
{txt}
{com}. 
. hist exposure_q15a2 if telephone==1 & wave==1, discrete percent gap(50) title("Exposure to Bomb Sirens") subtitle(`"CATI Panel"' `"(n=3016)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("") plotregion(margin(0 10 1 1)) name(g1, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a5_siren.jpg, replace
{txt}
{com}. 
. hist exposure_q15a3 if telephone==1 & wave==1, discrete percent gap(50) title("Exposure to Gunfire") subtitle(`"CATI Panel"' `"(n=3016)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("") plotregion(margin(0 10 1 1)) name(g1, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a5_gunfire.jpg, replace
{txt}
{com}. 
. hist exposure_q15a4 if telephone==1 & wave==1, discrete percent gap(50) title("Scared to Leave Home") subtitle(`"CATI Panel"' `"(n=3016)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("") plotregion(margin(0 10 1 1)) name(g1, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a5_scared.jpg, replace
{txt}
{com}. 
. hist exposure_q15a5 if telephone==1 & wave==1, discrete percent gap(50) title("Had to Go to a Shelter") subtitle(`"CATI Panel"' `"(n=3016)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("") plotregion(margin(0 10 1 1)) name(g1, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a5_shelter.jpg, replace
{txt}
{com}. 
. hist exposure_q15a6 if telephone==1 & wave==1, discrete percent gap(50) title("Seen People Wounded/Injured") subtitle(`"CATI Panel"' `"(n=3016)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("") plotregion(margin(0 10 1 1)) name(g1, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a5_seeninjured.jpg, replace
{txt}
{com}. 
. 
. ******* FIGURE A6 
. ***** These variables are named differently in the second phone survey.
. 
. replace q5=7 if telephone==1 & wave==2 & q5==8
{txt}(5 real changes made)

{com}. 
. hist q5 if telephone==1 & wave==2, discrete percent gap(50) title("Exposure to Gunfire") subtitle("(Phone Sample 2)") color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 "4-6 times" 5 "7-9 times" 6 "More than 10 times" 7 "DK/RA", alternate labsize(small)) xtitle("") plotregion(margin(0 10 1 1)) name(g1, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a6_gunfire.jpg, replace
{txt}
{com}. 
. gen q3_new=q3 if telephone==1 & wave==2
{txt}(6,474 missing values generated)

{com}. gen q4_new=q4 if telephone==1 & wave==2
{txt}(6,474 missing values generated)

{com}. 
. replace q3_new=. if q3_new==998 | q3_new==999 // RA or DK answers
{txt}(189 real changes made, 189 to missing)

{com}. replace q4_new=. if q4_new==998 | q4_new==999 // RA or DK answers
{txt}(138 real changes made, 138 to missing)

{com}. 
. hist q3_new, discrete percent xlabel(0(100)800) title("Injured Family and Friends") subtitle("(Phone Sample 2)") color(maroon) xtitle("")
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a6_injuredfamfriends.jpg, replace
{txt}
{com}. 
. hist q4_new, discrete percent xlabel(0(100)1000) title("Killed Family and Friends") subtitle("(Phone Sample 2)") color(maroon) xtitle("")
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a6_killedfamfriends.jpg, replace
{txt}
{com}. 
. 
. ****** FIGURE A7
. ****** Change the different scale of the second wave to make it consistent with the first wave  
. 
. hist exposure_q14a if online==1, discrete percent gap(20) title("Displacement") subtitle("(Online Panel)") color(maroon)  xlabel(1/3, valuelabel) xtitle("") note("Number of Respondents: 1729") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a7_displacement.jpg, replace
{txt}
{com}. 
. hist exposure_q14b if online==1, discrete percent gap(50) title("Did you leave ...?") subtitle("(Online Panel)") color(maroon)  xlabel(1 `""as a result of" "the violence""' 2 `""in anticipation" "of violence""' 3 "Refuse") xtitle("") note("Number of Respondents: 954") plotregion(margin(0 10 1 1)) 
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a7_didyouleave.jpg, replace
{txt}
{com}. 
. hist exposure_q15a1 if online==1 & wave==1, discrete percent gap(50) title("Exposure to Shelling") subtitle(`"Online Panel-Wave 1"' `"(n=1729)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("") note("Number of Respondents: 1729") plotregion(margin(0 10 1 1)) name(g1, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. gen exposure_q15c1_alt=exposure_q15c1
{txt}(6,758 missing values generated)

{com}. replace exposure_q15c1_alt=4 if exposure_q15c1_alt==5 | exposure_q15c1_alt==6 
{txt}(349 real changes made)

{com}. replace exposure_q15c1_alt=5 if exposure_q15c1_alt==7
{txt}(90 real changes made)

{com}. replace exposure_q15c1_alt=6 if exposure_q15c1_alt==8
{txt}(33 real changes made)

{com}. tab exposure_q15c1_alt exposure_q15c1

{txt}exposure_q {c |}    15a.1 How often in the past 2 weeks have you experienced the following? heard sh
  15c1_alt {c |}     never       once  2-3 times  4-6 times  7-9 times  10 times   Hard to s     Refuse {c |}     Total
{hline 11}{c +}{hline 88}{c +}{hline 10}
         1 {c |}{res}       681          0          0          0          0          0          0          0 {txt}{c |}{res}       681 
{txt}         2 {c |}{res}         0        203          0          0          0          0          0          0 {txt}{c |}{res}       203 
{txt}         3 {c |}{res}         0          0        257          0          0          0          0          0 {txt}{c |}{res}       257 
{txt}         4 {c |}{res}         0          0          0        116         58        291          0          0 {txt}{c |}{res}       465 
{txt}         5 {c |}{res}         0          0          0          0          0          0         90          0 {txt}{c |}{res}        90 
{txt}         6 {c |}{res}         0          0          0          0          0          0          0         33 {txt}{c |}{res}        33 
{txt}{hline 11}{c +}{hline 88}{c +}{hline 10}
     Total {c |}{res}       681        203        257        116         58        291         90         33 {txt}{c |}{res}     1,729 
{txt}
{com}. 
. hist exposure_q15c1_alt if online==1 & wave==2, discrete percent gap(50) title("Exposure to Shelling") subtitle(`"Online Panel-Wave 2"' `"(n=1729)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("") note("Number of Respondents: 1729") plotregion(margin(0 10 1 1)) name(g2, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. graph combine g1 g2, ycommon
{res}{txt}
{com}. 
. quietly graph export figure_a7_shelling.jpg, replace
{txt}
{com}. 
. hist exposure_q15a2 if online==1 & wave==1, discrete percent gap(50) title("Exposure to Bomb Sirens") subtitle(`"Online Panel-Wave 1"' `"(n=1729)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("") plotregion(margin(0 10 1 1)) name(g1, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. gen exposure_q15c2_alt=exposure_q15c2
{txt}(6,758 missing values generated)

{com}. replace exposure_q15c2_alt=4 if exposure_q15c2_alt==5 | exposure_q15c2_alt==6 
{txt}(1,013 real changes made)

{com}. replace exposure_q15c2_alt=5 if exposure_q15c2_alt==7
{txt}(49 real changes made)

{com}. replace exposure_q15c2_alt=6 if exposure_q15c2_alt==8
{txt}(22 real changes made)

{com}. tab exposure_q15c2_alt exposure_q15c2

{txt}exposure_q {c |}    15a.2 How often in the past 2 weeks have you experienced the following? heard bo
  15c2_alt {c |}     never       once  2-3 times  4-6 times  7-9 times  10 times   Hard to s     Refuse {c |}     Total
{hline 11}{c +}{hline 88}{c +}{hline 10}
         1 {c |}{res}       131          0          0          0          0          0          0          0 {txt}{c |}{res}       131 
{txt}         2 {c |}{res}         0         48          0          0          0          0          0          0 {txt}{c |}{res}        48 
{txt}         3 {c |}{res}         0          0        214          0          0          0          0          0 {txt}{c |}{res}       214 
{txt}         4 {c |}{res}         0          0          0        252        160        853          0          0 {txt}{c |}{res}     1,265 
{txt}         5 {c |}{res}         0          0          0          0          0          0         49          0 {txt}{c |}{res}        49 
{txt}         6 {c |}{res}         0          0          0          0          0          0          0         22 {txt}{c |}{res}        22 
{txt}{hline 11}{c +}{hline 88}{c +}{hline 10}
     Total {c |}{res}       131         48        214        252        160        853         49         22 {txt}{c |}{res}     1,729 
{txt}
{com}. 
. hist exposure_q15c2_alt if online==1 & wave==2, discrete percent gap(50) title("Exposure to Bomb Sirens") subtitle(`"Online Panel-Wave 2"' `"(n=1729)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("")  plotregion(margin(0 10 1 1)) name(g2, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. graph combine g1 g2, ycommon
{res}{txt}
{com}. 
. quietly graph export figure_a7_siren.jpg, replace
{txt}
{com}. 
. hist exposure_q15a3 if online==1 & wave==1, discrete percent gap(50) title("Exposure to Gunfire") subtitle(`"Online Panel-Wave 1"' `"(n=1729)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("") plotregion(margin(0 10 1 1)) name(g1, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. gen exposure_q15c3_alt=exposure_q15c3
{txt}(6,758 missing values generated)

{com}. replace exposure_q15c3_alt=4 if exposure_q15c3_alt==5 | exposure_q15c3_alt==6 
{txt}(141 real changes made)

{com}. replace exposure_q15c3_alt=5 if exposure_q15c3_alt==7
{txt}(117 real changes made)

{com}. replace exposure_q15c3_alt=6 if exposure_q15c3_alt==8
{txt}(34 real changes made)

{com}. tab exposure_q15c3_alt exposure_q15c3

{txt}exposure_q {c |}    15a.3 How often in the past 2 weeks have you experienced the following? heard gu
  15c3_alt {c |}     never       once  2-3 times  4-6 times  7-9 times  10 times   Hard to s     Refuse {c |}     Total
{hline 11}{c +}{hline 88}{c +}{hline 10}
         1 {c |}{res}     1,112          0          0          0          0          0          0          0 {txt}{c |}{res}     1,112 
{txt}         2 {c |}{res}         0        142          0          0          0          0          0          0 {txt}{c |}{res}       142 
{txt}         3 {c |}{res}         0          0        120          0          0          0          0          0 {txt}{c |}{res}       120 
{txt}         4 {c |}{res}         0          0          0         63         49         92          0          0 {txt}{c |}{res}       204 
{txt}         5 {c |}{res}         0          0          0          0          0          0        117          0 {txt}{c |}{res}       117 
{txt}         6 {c |}{res}         0          0          0          0          0          0          0         34 {txt}{c |}{res}        34 
{txt}{hline 11}{c +}{hline 88}{c +}{hline 10}
     Total {c |}{res}     1,112        142        120         63         49         92        117         34 {txt}{c |}{res}     1,729 
{txt}
{com}. 
. hist exposure_q15c3_alt if online==1 & wave==2, discrete percent gap(50) title("Exposure to Gunfire") subtitle(`"Online Panel-Wave 2"' `"(n=1729)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("")  plotregion(margin(0 10 1 1)) name(g2, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. graph combine g1 g2, ycommon
{res}{txt}
{com}. 
. quietly graph export figure_a7_gunfire.jpg, replace
{txt}
{com}. 
. hist exposure_q15a4 if online==1 & wave==1, discrete percent gap(50) title("Scared to Leave Home") subtitle(`"Online Panel-Wave 1"' `"(n=1729)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("") plotregion(margin(0 10 1 1)) name(g1, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. gen exposure_q15c4_alt=exposure_q15c4
{txt}(6,758 missing values generated)

{com}. replace exposure_q15c4_alt=4 if exposure_q15c4_alt==5 | exposure_q15c4_alt==6 
{txt}(279 real changes made)

{com}. replace exposure_q15c4_alt=5 if exposure_q15c4_alt==7
{txt}(157 real changes made)

{com}. replace exposure_q15c4_alt=6 if exposure_q15c4_alt==8
{txt}(21 real changes made)

{com}. tab exposure_q15c4_alt exposure_q15c4

{txt}exposure_q {c |}    15a.4 How often in the past 2 weeks have you experienced the following? been sca
  15c4_alt {c |}     never       once  2-3 times  4-6 times  7-9 times  10 times   Hard to s     Refuse {c |}     Total
{hline 11}{c +}{hline 88}{c +}{hline 10}
         1 {c |}{res}       815          0          0          0          0          0          0          0 {txt}{c |}{res}       815 
{txt}         2 {c |}{res}         0        170          0          0          0          0          0          0 {txt}{c |}{res}       170 
{txt}         3 {c |}{res}         0          0        209          0          0          0          0          0 {txt}{c |}{res}       209 
{txt}         4 {c |}{res}         0          0          0         78         38        241          0          0 {txt}{c |}{res}       357 
{txt}         5 {c |}{res}         0          0          0          0          0          0        157          0 {txt}{c |}{res}       157 
{txt}         6 {c |}{res}         0          0          0          0          0          0          0         21 {txt}{c |}{res}        21 
{txt}{hline 11}{c +}{hline 88}{c +}{hline 10}
     Total {c |}{res}       815        170        209         78         38        241        157         21 {txt}{c |}{res}     1,729 
{txt}
{com}. 
. hist exposure_q15c4_alt if online==1 & wave==2, discrete percent gap(50) title("Scared to Leave Home") subtitle(`"Online Panel-Wave 2"' `"(n=1729)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("")  plotregion(margin(0 10 1 1)) name(g2, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. graph combine g1 g2, ycommon
{res}{txt}
{com}. 
. quietly graph export figure_a7_scared.jpg, replace
{txt}
{com}. 
. hist exposure_q15a5 if online==1 & wave==1, discrete percent gap(50) title("Had to Go to a Shelter") subtitle(`"Online Panel-Wave 1"' `"(n=1729)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("") plotregion(margin(0 10 1 1)) name(g1, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. gen exposure_q15c5_alt=exposure_q15c5
{txt}(6,758 missing values generated)

{com}. replace exposure_q15c5_alt=4 if exposure_q15c5_alt==5 | exposure_q15c5_alt==6 
{txt}(294 real changes made)

{com}. replace exposure_q15c5_alt=5 if exposure_q15c5_alt==7
{txt}(136 real changes made)

{com}. replace exposure_q15c5_alt=6 if exposure_q15c5_alt==8
{txt}(31 real changes made)

{com}. tab exposure_q15c5_alt exposure_q15c5

{txt}exposure_q {c |}    15a.5 How often in the past 2 weeks have you experienced the following? had to g
  15c5_alt {c |}     never       once  2-3 times  4-6 times  7-9 times  10 times   Hard to s     Refuse {c |}     Total
{hline 11}{c +}{hline 88}{c +}{hline 10}
         1 {c |}{res}       766          0          0          0          0          0          0          0 {txt}{c |}{res}       766 
{txt}         2 {c |}{res}         0        154          0          0          0          0          0          0 {txt}{c |}{res}       154 
{txt}         3 {c |}{res}         0          0        211          0          0          0          0          0 {txt}{c |}{res}       211 
{txt}         4 {c |}{res}         0          0          0        137         50        244          0          0 {txt}{c |}{res}       431 
{txt}         5 {c |}{res}         0          0          0          0          0          0        136          0 {txt}{c |}{res}       136 
{txt}         6 {c |}{res}         0          0          0          0          0          0          0         31 {txt}{c |}{res}        31 
{txt}{hline 11}{c +}{hline 88}{c +}{hline 10}
     Total {c |}{res}       766        154        211        137         50        244        136         31 {txt}{c |}{res}     1,729 
{txt}
{com}. 
. hist exposure_q15c5_alt if online==1 & wave==2, discrete percent gap(50) title("Had to Go to a Shelter") subtitle(`"Online Panel-Wave 2"' `"(n=1729)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("")  plotregion(margin(0 10 1 1)) name(g2, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. graph combine g1 g2, ycommon
{res}{txt}
{com}. 
. quietly graph export figure_a7_shelter.jpg, replace
{txt}
{com}. 
. hist exposure_q15a6 if online==1 & wave==1, discrete percent gap(50) title("Seen People Wounded/Injured") subtitle(`"Online Panel-Wave 1"' `"(n=1729)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("") plotregion(margin(0 10 1 1)) name(g1, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. gen exposure_q15c6_alt=exposure_q15c6
{txt}(6,758 missing values generated)

{com}. replace exposure_q15c6_alt=4 if exposure_q15c6_alt==5 | exposure_q15c6_alt==6 
{txt}(94 real changes made)

{com}. replace exposure_q15c6_alt=5 if exposure_q15c6_alt==7
{txt}(111 real changes made)

{com}. replace exposure_q15c6_alt=6 if exposure_q15c6_alt==8
{txt}(40 real changes made)

{com}. tab exposure_q15c6_alt exposure_q15c6

{txt}exposure_q {c |}    15a.6 How often in the past 2 weeks have you experienced the following? seen peo
  15c6_alt {c |}     never       once  2-3 times  4-6 times  7-9 times  10 times   Hard to s     Refuse {c |}     Total
{hline 11}{c +}{hline 88}{c +}{hline 10}
         1 {c |}{res}     1,098          0          0          0          0          0          0          0 {txt}{c |}{res}     1,098 
{txt}         2 {c |}{res}         0        178          0          0          0          0          0          0 {txt}{c |}{res}       178 
{txt}         3 {c |}{res}         0          0        156          0          0          0          0          0 {txt}{c |}{res}       156 
{txt}         4 {c |}{res}         0          0          0         52         23         71          0          0 {txt}{c |}{res}       146 
{txt}         5 {c |}{res}         0          0          0          0          0          0        111          0 {txt}{c |}{res}       111 
{txt}         6 {c |}{res}         0          0          0          0          0          0          0         40 {txt}{c |}{res}        40 
{txt}{hline 11}{c +}{hline 88}{c +}{hline 10}
     Total {c |}{res}     1,098        178        156         52         23         71        111         40 {txt}{c |}{res}     1,729 
{txt}
{com}. 
. hist exposure_q15c6_alt if online==1 & wave==2, discrete percent gap(50) title("Seen People Wounded/Injured") subtitle(`"Online Panel-Wave 2"' `"(n=1729)"') color(maroon)  xlabel(1 "Never" 2 "Once" 3 "2-3 times" 4 " Near daily" 5 "Hard to say" 6 "Refuse", alternate labsize(small)) xtitle("")  plotregion(margin(0 10 1 1)) name(g2, replace)
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. graph combine g1 g2, ycommon
{res}{txt}
{com}. 
. quietly graph export figure_a7_seeninjured.jpg, replace
{txt}
{com}. 
. 
. ****** FIGURE A-13
. 
. sort id wave
{txt}
{com}. 
. 
. foreach v in shelling_exposure bombsiren_exposure gunfire_exposure shelterinplace_exposure bombshelter_exposure vizwounded_exposure{c -(}
{txt}  2{com}. 
. gen `v'_wave2=`v'[_n+1] if id==id[_n+1]
{txt}  3{com}. 
. tab `v'_wave2, m
{txt}  4{com}. tab `v' if online==1 & wave==2, m
{txt}  5{com}. 
. 
. gen diff_`v'=`v'_wave2-`v' if online==1 & wave==1
{txt}  6{com}. tab diff_`v', m
{txt}  7{com}. 
. 
. gen diff_`v'2=.
{txt}  8{com}. replace diff_`v'2=-1 if diff_`v'<0 & !missing(diff_`v') 
{txt}  9{com}. replace diff_`v'2=0 if diff_`v'==0 & !missing(diff_`v') 
{txt} 10{com}. replace diff_`v'2=1 if diff_`v'>0 & !missing(diff_`v') 
{txt} 11{com}. tab diff_`v'2, 
{txt} 12{com}. {c )-}
{txt}(6,881 missing values generated)

shelling_ex {c |}
posure_wave {c |}
          2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        681        8.02        8.02
{txt}          2 {c |}{res}        203        2.39       10.42
{txt}          3 {c |}{res}        257        3.03       13.44
{txt}          4 {c |}{res}        465        5.48       18.92
{txt}          . {c |}{res}      6,881       81.08      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00

   {txt}Shelling {c |}
   exposure {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        681       39.39       39.39
{txt}          2 {c |}{res}        203       11.74       51.13
{txt}          3 {c |}{res}        257       14.86       65.99
{txt}          4 {c |}{res}        465       26.89       92.89
{txt}          . {c |}{res}        123        7.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,729      100.00
{txt}(7,036 missing values generated)

diff_shelli {c |}
ng_exposure {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -3 {c |}{res}         25        0.29        0.29
{txt}         -2 {c |}{res}         68        0.80        1.10
{txt}         -1 {c |}{res}        150        1.77        2.86
{txt}          0 {c |}{res}        812        9.57       12.43
{txt}          1 {c |}{res}        226        2.66       15.09
{txt}          2 {c |}{res}         97        1.14       16.24
{txt}          3 {c |}{res}         73        0.86       17.10
{txt}          . {c |}{res}      7,036       82.90      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00
{txt}(8,487 missing values generated)
(243 real changes made)
(812 real changes made)
(396 real changes made)

diff_shelli {c |}
ng_exposure {c |}
          2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}        243       16.75       16.75
{txt}          0 {c |}{res}        812       55.96       72.71
{txt}          1 {c |}{res}        396       27.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,451      100.00
{txt}(6,829 missing values generated)

bombsiren_e {c |}
xposure_wav {c |}
         e2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        131        1.54        1.54
{txt}          2 {c |}{res}         48        0.57        2.11
{txt}          3 {c |}{res}        214        2.52        4.63
{txt}          4 {c |}{res}      1,265       14.91       19.54
{txt}          . {c |}{res}      6,829       80.46      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00

 {txt}Bomb siren {c |}
   exposure {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        131        7.58        7.58
{txt}          2 {c |}{res}         48        2.78       10.35
{txt}          3 {c |}{res}        214       12.38       22.73
{txt}          4 {c |}{res}      1,265       73.16       95.89
{txt}          . {c |}{res}         71        4.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,729      100.00
{txt}(6,877 missing values generated)

diff_bombsi {c |}
ren_exposur {c |}
          e {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -3 {c |}{res}         16        0.19        0.19
{txt}         -2 {c |}{res}         26        0.31        0.49
{txt}         -1 {c |}{res}         94        1.11        1.60
{txt}          0 {c |}{res}      1,079       12.71       14.32
{txt}          1 {c |}{res}        298        3.51       17.83
{txt}          2 {c |}{res}         48        0.57       18.39
{txt}          3 {c |}{res}         49        0.58       18.97
{txt}          . {c |}{res}      6,877       81.03      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00
{txt}(8,487 missing values generated)
(136 real changes made)
(1,079 real changes made)
(395 real changes made)

diff_bombsi {c |}
ren_exposur {c |}
         e2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}        136        8.45        8.45
{txt}          0 {c |}{res}      1,079       67.02       75.47
{txt}          1 {c |}{res}        395       24.53      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,610      100.00
{txt}(6,909 missing values generated)

gunfire_exp {c |}
osure_wave2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}      1,112       13.10       13.10
{txt}          2 {c |}{res}        142        1.67       14.78
{txt}          3 {c |}{res}        120        1.41       16.19
{txt}          4 {c |}{res}        204        2.40       18.59
{txt}          . {c |}{res}      6,909       81.41      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00

    {txt}Gunfire {c |}
   exposure {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}      1,112       64.31       64.31
{txt}          2 {c |}{res}        142        8.21       72.53
{txt}          3 {c |}{res}        120        6.94       79.47
{txt}          4 {c |}{res}        204       11.80       91.27
{txt}          . {c |}{res}        151        8.73      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,729      100.00
{txt}(7,028 missing values generated)

diff_gunfir {c |}
 e_exposure {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -3 {c |}{res}         16        0.19        0.19
{txt}         -2 {c |}{res}         45        0.53        0.72
{txt}         -1 {c |}{res}         80        0.94        1.66
{txt}          0 {c |}{res}      1,040       12.25       13.92
{txt}          1 {c |}{res}        143        1.68       15.60
{txt}          2 {c |}{res}         73        0.86       16.46
{txt}          3 {c |}{res}         62        0.73       17.19
{txt}          . {c |}{res}      7,028       82.81      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00
{txt}(8,487 missing values generated)
(141 real changes made)
(1,040 real changes made)
(278 real changes made)

diff_gunfir {c |}
e_exposure2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}        141        9.66        9.66
{txt}          0 {c |}{res}      1,040       71.28       80.95
{txt}          1 {c |}{res}        278       19.05      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,459      100.00
{txt}(6,936 missing values generated)

shelterinpl {c |}
ace_exposur {c |}
    e_wave2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        815        9.60        9.60
{txt}          2 {c |}{res}        170        2.00       11.61
{txt}          3 {c |}{res}        209        2.46       14.07
{txt}          4 {c |}{res}        357        4.21       18.28
{txt}          . {c |}{res}      6,936       81.72      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00

  {txt}Scared to {c |}
 leave home {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        815       47.14       47.14
{txt}          2 {c |}{res}        170        9.83       56.97
{txt}          3 {c |}{res}        209       12.09       69.06
{txt}          4 {c |}{res}        357       20.65       89.71
{txt}          . {c |}{res}        178       10.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,729      100.00
{txt}(7,088 missing values generated)

diff_shelte {c |}
rinplace_ex {c |}
     posure {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -3 {c |}{res}         20        0.24        0.24
{txt}         -2 {c |}{res}         59        0.70        0.93
{txt}         -1 {c |}{res}         94        1.11        2.04
{txt}          0 {c |}{res}        860       10.13       12.17
{txt}          1 {c |}{res}        187        2.20       14.37
{txt}          2 {c |}{res}        112        1.32       15.69
{txt}          3 {c |}{res}         67        0.79       16.48
{txt}          . {c |}{res}      7,088       83.52      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00
{txt}(8,487 missing values generated)
(173 real changes made)
(860 real changes made)
(366 real changes made)

diff_shelte {c |}
rinplace_ex {c |}
    posure2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}        173       12.37       12.37
{txt}          0 {c |}{res}        860       61.47       73.84
{txt}          1 {c |}{res}        366       26.16      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,399      100.00
{txt}(6,925 missing values generated)

bombshelter {c |}
_exposure_w {c |}
       ave2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        766        9.03        9.03
{txt}          2 {c |}{res}        154        1.81       10.84
{txt}          3 {c |}{res}        211        2.49       13.33
{txt}          4 {c |}{res}        431        5.08       18.40
{txt}          . {c |}{res}      6,925       81.60      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00

       {txt}Bomb {c |}
shelter use {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        766       44.30       44.30
{txt}          2 {c |}{res}        154        8.91       53.21
{txt}          3 {c |}{res}        211       12.20       65.41
{txt}          4 {c |}{res}        431       24.93       90.34
{txt}          . {c |}{res}        167        9.66      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,729      100.00
{txt}(7,044 missing values generated)

diff_bombsh {c |}
elter_expos {c |}
        ure {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -3 {c |}{res}         28        0.33        0.33
{txt}         -2 {c |}{res}         60        0.71        1.04
{txt}         -1 {c |}{res}         99        1.17        2.20
{txt}          0 {c |}{res}        829        9.77       11.97
{txt}          1 {c |}{res}        244        2.87       14.85
{txt}          2 {c |}{res}         94        1.11       15.95
{txt}          3 {c |}{res}         89        1.05       17.00
{txt}          . {c |}{res}      7,044       83.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00
{txt}(8,487 missing values generated)
(187 real changes made)
(829 real changes made)
(427 real changes made)

diff_bombsh {c |}
elter_expos {c |}
       ure2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}        187       12.96       12.96
{txt}          0 {c |}{res}        829       57.45       70.41
{txt}          1 {c |}{res}        427       29.59      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,443      100.00
{txt}(6,909 missing values generated)

vizwounded_ {c |}
exposure_wa {c |}
        ve2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}      1,098       12.94       12.94
{txt}          2 {c |}{res}        178        2.10       15.03
{txt}          3 {c |}{res}        156        1.84       16.87
{txt}          4 {c |}{res}        146        1.72       18.59
{txt}          . {c |}{res}      6,909       81.41      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00

     {txt}Seeing {c |}
    wounded {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}      1,098       63.50       63.50
{txt}          2 {c |}{res}        178       10.29       73.80
{txt}          3 {c |}{res}        156        9.02       82.82
{txt}          4 {c |}{res}        146        8.44       91.27
{txt}          . {c |}{res}        151        8.73      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,729      100.00
{txt}(7,002 missing values generated)

diff_vizwou {c |}
nded_exposu {c |}
         re {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -3 {c |}{res}         14        0.16        0.16
{txt}         -2 {c |}{res}         44        0.52        0.68
{txt}         -1 {c |}{res}         97        1.14        1.83
{txt}          0 {c |}{res}      1,054       12.42       14.25
{txt}          1 {c |}{res}        157        1.85       16.10
{txt}          2 {c |}{res}         78        0.92       17.01
{txt}          3 {c |}{res}         41        0.48       17.50
{txt}          . {c |}{res}      7,002       82.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00
{txt}(8,487 missing values generated)
(155 real changes made)
(1,054 real changes made)
(276 real changes made)

diff_vizwou {c |}
nded_exposu {c |}
        re2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}        155       10.44       10.44
{txt}          0 {c |}{res}      1,054       70.98       81.41
{txt}          1 {c |}{res}        276       18.59      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,485      100.00
{txt}
{com}. 
. 
. hist diff_shelling_exposure2, discrete percent gap(50) title("Change in Exposure to Shelling") graphregion(fcolor(ltbluishgray)) color(maroon)  xlabel(-1 "Decrease" 0 "Same" 1 "Increase", ) xtitle("") plotregion(margin(0 10 1 1)) 
{txt}(start={res}-1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a13_shelling.jpg, replace
{txt}
{com}. 
. hist diff_bombsiren_exposure2, discrete percent gap(50) title("Change in Exposure to Bomb Siren") graphregion(fcolor(ltbluishgray)) color(maroon)  xlabel(-1 "Decrease" 0 "Same" 1 "Increase", ) xtitle("") plotregion(margin(0 10 1 1)) 
{txt}(start={res}-1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a13_siren.jpg, replace
{txt}
{com}. 
. hist diff_gunfire_exposure2, discrete percent gap(50) title("Change in Exposure to Gunfire") graphregion(fcolor(ltbluishgray)) color(maroon)  xlabel(-1 "Decrease" 0 "Same" 1 "Increase", ) xtitle("") plotregion(margin(0 10 1 1)) 
{txt}(start={res}-1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a13_gunfire.jpg, replace
{txt}
{com}. 
. hist diff_shelterinplace_exposure2, discrete percent gap(50) title("Change in Scared to Leave Home") graphregion(fcolor(ltbluishgray)) color(maroon)  xlabel(-1 "Decrease" 0 "Same" 1 "Increase", ) xtitle("") plotregion(margin(0 10 1 1)) 
{txt}(start={res}-1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a13_scared.jpg, replace
{txt}
{com}. 
. hist diff_bombshelter_exposure2, discrete percent gap(50) title("Change in Had to Go to a Shelter") graphregion(fcolor(ltbluishgray)) color(maroon)  xlabel(-1 "Decrease" 0 "Same" 1 "Increase",) xtitle("") plotregion(margin(0 10 1 1)) 
{txt}(start={res}-1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a13_shelter.jpg, replace
{txt}
{com}. 
. hist diff_vizwounded_exposure2, discrete percent gap(50) title("Change in Seen People Wounded/Injured") graphregion(fcolor(ltbluishgray)) color(maroon)  xlabel(-1 "Decrease" 0 "Same" 1 "Increase", ) xtitle("") plotregion(margin(0 10 1 1)) 
{txt}(start={res}-1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. quietly graph export figure_a13_seeninjured.jpg, replace
{txt}
{com}. 
. 
.         
. ****** FIGURE A-14a
. 
. 
. gen shelling_exposure_since=exposure_q15b1 if wave==1 & online==1
{txt}(6,758 missing values generated)

{com}.                 
. gen bombsiren_exposure_since=exposure_q15b2 if wave==1 & online==1
{txt}(6,758 missing values generated)

{com}.         
. gen gunfire_exposure_since=exposure_q15b3 if wave==1 & online==1
{txt}(6,758 missing values generated)

{com}. 
. gen shelterinplace_exposure_since=exposure_q15b4 if wave==1 & online==1
{txt}(6,758 missing values generated)

{com}. 
. gen bombshelter_exposure_since=exposure_q15b5 if wave==1 & online==1
{txt}(6,758 missing values generated)

{com}. 
. gen vizwounded_exposure_since=exposure_q15b6 if wave==1 & online==1
{txt}(6,758 missing values generated)

{com}.         
. // Normalizing long-run exposure to violence 
.         
.         foreach v in shelling_exposure_since bombsiren_exposure_since gunfire_exposure_since shelterinplace_exposure_since bombshelter_exposure_since vizwounded_exposure_since{c -(}
{txt}  2{com}.                 
.                 replace `v'=. if wave==1 & online==1 & `v'>=8
{txt}  3{com}.                 gen `v'_n=(`v'-1)/6 if wave==1 & online==1 
{txt}  4{com}.         {c )-}
{txt}(164 real changes made, 164 to missing)
(6,922 missing values generated)
(84 real changes made, 84 to missing)
(6,842 missing values generated)
(183 real changes made, 183 to missing)
(6,941 missing values generated)
(224 real changes made, 224 to missing)
(6,982 missing values generated)
(167 real changes made, 167 to missing)
(6,925 missing values generated)
(208 real changes made, 208 to missing)
(6,966 missing values generated)

{com}. 
.         tab shelling_exposure_since_n, m

{txt}shelling_ex {c |}
posure_sinc {c |}
        e_n {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        225        2.65        2.65
{txt}   .1666667 {c |}{res}        103        1.21        3.86
{txt}   .3333333 {c |}{res}        207        2.44        6.30
{txt}         .5 {c |}{res}        154        1.81        8.12
{txt}   .6666667 {c |}{res}         98        1.15        9.27
{txt}   .8333333 {c |}{res}        122        1.44       10.71
{txt}          1 {c |}{res}        656        7.73       18.44
{txt}          . {c |}{res}      6,922       81.56      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00
{txt}
{com}.         tab bombsiren_exposure_since_n, m

{txt}bombsiren_e {c |}
xposure_sin {c |}
       ce_n {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         40        0.47        0.47
{txt}   .1666667 {c |}{res}          9        0.11        0.58
{txt}   .3333333 {c |}{res}         38        0.45        1.03
{txt}         .5 {c |}{res}         46        0.54        1.57
{txt}   .6666667 {c |}{res}         35        0.41        1.98
{txt}   .8333333 {c |}{res}         40        0.47        2.45
{txt}          1 {c |}{res}      1,437       16.93       19.38
{txt}          . {c |}{res}      6,842       80.62      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00
{txt}
{com}.         tab gunfire_exposure_since_n, m

{txt}gunfire_exp {c |}
osure_since {c |}
         _n {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        660        7.78        7.78
{txt}   .1666667 {c |}{res}        119        1.40        9.18
{txt}   .3333333 {c |}{res}        210        2.47       11.65
{txt}         .5 {c |}{res}        116        1.37       13.02
{txt}   .6666667 {c |}{res}         69        0.81       13.83
{txt}   .8333333 {c |}{res}         88        1.04       14.87
{txt}          1 {c |}{res}        284        3.35       18.22
{txt}          . {c |}{res}      6,941       81.78      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00
{txt}
{com}.         tab shelterinplace_exposure_since_n, m

{txt}shelterinpl {c |}
ace_exposur {c |}
  e_since_n {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        396        4.67        4.67
{txt}   .1666667 {c |}{res}         66        0.78        5.44
{txt}   .3333333 {c |}{res}        243        2.86        8.31
{txt}         .5 {c |}{res}        120        1.41        9.72
{txt}   .6666667 {c |}{res}         77        0.91       10.63
{txt}   .8333333 {c |}{res}        105        1.24       11.87
{txt}          1 {c |}{res}        498        5.87       17.73
{txt}          . {c |}{res}      6,982       82.27      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00
{txt}
{com}.         tab bombshelter_exposure_since_n, m

{txt}bombshelter {c |}
_exposure_s {c |}
     ince_n {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        383        4.51        4.51
{txt}   .1666667 {c |}{res}         96        1.13        5.64
{txt}   .3333333 {c |}{res}        172        2.03        7.67
{txt}         .5 {c |}{res}        119        1.40        9.07
{txt}   .6666667 {c |}{res}         93        1.10       10.17
{txt}   .8333333 {c |}{res}        116        1.37       11.54
{txt}          1 {c |}{res}        583        6.87       18.40
{txt}          . {c |}{res}      6,925       81.60      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00
{txt}
{com}.         tab vizwounded_exposure_since_n, m

{txt}vizwounded_ {c |}
exposure_si {c |}
      nce_n {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        884       10.42       10.42
{txt}   .1666667 {c |}{res}        139        1.64       12.05
{txt}   .3333333 {c |}{res}        183        2.16       14.21
{txt}         .5 {c |}{res}         85        1.00       15.21
{txt}   .6666667 {c |}{res}         47        0.55       15.77
{txt}   .8333333 {c |}{res}         45        0.53       16.30
{txt}          1 {c |}{res}        138        1.63       17.92
{txt}          . {c |}{res}      6,966       82.08      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      8,487      100.00
{txt}
{com}.         
.         
. // violence exposure, PCA ***Telephone wave 2 ther PCA is missing value because we have data only on exposure to gunfire
.         pca shelling_exposure_since_n bombsiren_exposure_since_n gunfire_exposure_since_n shelterinplace_exposure_since_n bombshelter_exposure_since_n vizwounded_exposure_since_n, comp(1) 

{txt}Principal components/correlation{col 50}Number of obs    = {res}     1,258
{col 50}{txt}Number of comp.  = {res}         1
{col 50}{txt}Trace            {col 67}=  {res}        6
{col 5}{txt}Rotation: (unrotated = principal){col 50}Rho              = {res}    0.3970

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}   Component {c |}   Eigenvalue   Difference         Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 12:Comp1} {c |}{res}      2.38206      1.36433             0.3970       0.3970
{txt}{col 5}{ralign 12:Comp2} {c |}{res}      1.01773     .0578694             0.1696       0.5666
{txt}{col 5}{ralign 12:Comp3} {c |}{res}      .959859       .16291             0.1600       0.7266
{txt}{col 5}{ralign 12:Comp4} {c |}{res}      .796949      .339035             0.1328       0.8594
{txt}{col 5}{ralign 12:Comp5} {c |}{res}      .457913     .0724252             0.0763       0.9358
{txt}{col 5}{ralign 12:Comp6} {c |}{res}      .385488            .             0.0642       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}

Principal components (eigenvectors) 

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 13}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Comp1}{space 1}{c |}{space 1}{ralign 11:Unexplained}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 13}
{space 4}{space 0}{ralign 12:shelling_e~n}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4495}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .5187}}}{space 1}
{space 4}{space 0}{ralign 12:bombsiren_~n}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2329}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .8708}}}{space 1}
{space 4}{space 0}{ralign 12:gunfire_ex~n}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4713}}}{space 1}{c |}{space 1}{center 11:{res:{sf:       .471}}}{space 1}
{space 4}{space 0}{ralign 12:shelterinp~n}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4616}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .4925}}}{space 1}
{space 4}{space 0}{ralign 12:bombshelte~n}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4017}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .6156}}}{space 1}
{space 4}{space 0}{ralign 12:vizwounded~n}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3837}}}{space 1}{c |}{space 1}{center 11:{res:{sf:      .6493}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 13}

{com}.                 predict pcsince1
{txt}({bf:score} assumed)

Scoring coefficients 
{col 5}sum of squares(column-loading) = 1

{space 4}{hline 13}{c  TT}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Comp1}{space 1}
{space 4}{hline 13}{c   +}{hline 10}
{space 4}{space 0}{ralign 12:shelling_e~n}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4495}}}{space 1}
{space 4}{space 0}{ralign 12:bombsiren_~n}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2329}}}{space 1}
{space 4}{space 0}{ralign 12:gunfire_ex~n}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4713}}}{space 1}
{space 4}{space 0}{ralign 12:shelterinp~n}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4616}}}{space 1}
{space 4}{space 0}{ralign 12:bombshelte~n}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4017}}}{space 1}
{space 4}{space 0}{ralign 12:vizwounded~n}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3837}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}

{com}.                 rename pcsince1 pca_sinceviolexposure
{res}{txt}
{com}. sum pca_sinceviolexposure

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
pca_sincev~e {c |}{res}      1,258    2.46e-09    1.543393  -3.562864    3.20969
{txt}
{com}.                 
.                 
. ***STANDARTIZING TO LIE BETWEEN 0-1
. 
. foreach v of varlist pca_sinceviolexposure {c -(}
{txt}  2{com}.     qui summ `v'
{txt}  3{com}.     gen `v'0_1 = (`v' - r(min)) / (r(max) - r(min))
{txt}  4{com}.         sum `v'0_1
{txt}  5{com}.         sum `v'
{txt}  6{com}. {c )-}
{txt}(7,229 missing values generated)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
pca_sincev~1 {c |}{res}      1,258    .5260739    .2278894          0          1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
pca_sincev~e {c |}{res}      1,258    2.46e-09    1.543393  -3.562864    3.20969
{txt}
{com}. 
. sort id wave
{txt}
{com}. 
. replace pca_sinceviolexposure0_1=pca_sinceviolexposure0_1[_n-1] if id[_n]==id[_n-1]
{txt}(1,258 real changes made)

{com}. 
. 
. 
. **REGRESSING THE PRINCIPLES OF NEGOTIATIONS ON PCA OF VIOLENCE EXPOSURE
. 
. 
. eststo clear 
{txt}
{com}.         
. foreach v in fd_pca_principlesneg0_1 fd_q13_norm fd_q14_norm fd_q15_norm fd_q16_norm fd_q17_norm fd_q18_norm fd_q19_norm fd_q20_norm{c -(}
{txt}  2{com}.         eststo m`v'se: reghdfe `v' fd_pca_violexposure0_1 pca_sinceviolexposure0_1, a(oblast_lag) vce(cluster oblast_lag)
{txt}  3{com}.         margins, dydx(fd_pca_violexposure0_1) df(24) post
{txt}  4{com}.         eststo m`v'sh
{txt}  5{com}.         est restore m`v'se
{txt}  6{com}.         margins, dydx(pca_sinceviolexposure0_1) df(24) post
{txt}  7{com}.         eststo m`v'si
{txt}  8{com}.         esttab, beta not 
{txt}  9{com}. {c )-}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       569
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      1.31
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.2876
{txt}{col 51}R-squared{col 67}= {res}    0.0323
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0141
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0032
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1232

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1} fd_pca_principlesneg0_1{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .0438096{col 38}{space 2} .0445366{col 49}{space 1}    0.98{col 58}{space 3}0.335{col 66}{space 4}-.0481095{col 79}{space 3} .1357286
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2} .0234823{col 38}{space 2} .0212028{col 49}{space 1}    1.11{col 58}{space 3}0.279{col 66}{space 4}-.0202782{col 79}{space 3} .0672427
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} .3587017{col 38}{space 2} .0247397{col 49}{space 1}   14.50{col 58}{space 3}0.000{col 66}{space 4} .3076415{col 79}{space 3} .4097618
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:569}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0438096{col 36}{space 2} .0445366{col 47}{space 1}    0.98{col 56}{space 3}0.335{col 64}{space 4}-.0481095{col 77}{space 3} .1357286
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_pca_principlesneg0_1se:mfd_pca_principlesneg0_1se} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:569}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2} .0234823{col 38}{space 2} .0212028{col 49}{space 1}    1.11{col 58}{space 3}0.279{col 66}{space 4}-.0202782{col 79}{space 3} .0672427
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 60}
{txt}                      (1)             (2)             (3)   
{txt}             fd_pca_pri~1                                   
{txt}{hline 60}
{txt}fd_pca_vio~1{res}        0.040                                   {txt}
{txt}pca_sincev~1{res}        0.043                                   {txt}
{txt}{hline 60}
{txt}N           {res}          569             569             569   {txt}
{txt}{hline 60}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       845
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      0.85
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.4392
{txt}{col 51}R-squared{col 67}= {res}    0.0242
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0068
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0014
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.3058

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q13_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .0094288{col 38}{space 2} .0989574{col 49}{space 1}    0.10{col 58}{space 3}0.925{col 66}{space 4}-.1948093{col 79}{space 3} .2136669
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2} -.055953{col 38}{space 2} .0501172{col 49}{space 1}   -1.12{col 58}{space 3}0.275{col 66}{space 4}-.1593898{col 79}{space 3} .0474838
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} .0258588{col 38}{space 2} .0436855{col 49}{space 1}    0.59{col 58}{space 3}0.559{col 66}{space 4}-.0643036{col 79}{space 3} .1160212
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:845}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0094288{col 36}{space 2} .0989574{col 47}{space 1}    0.10{col 56}{space 3}0.925{col 64}{space 4}-.1948093{col 77}{space 3} .2136669
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q13_normse:mfd_q13_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:845}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2} -.055953{col 38}{space 2} .0501172{col 49}{space 1}   -1.12{col 58}{space 3}0.275{col 66}{space 4}-.1593898{col 79}{space 3} .0474838
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 108}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)   
{txt}             fd_pca_pri~1                                     fd_q13_norm                                   
{txt}{hline 108}
{txt}fd_pca_vio~1{res}        0.040                                           0.003                                   {txt}
{txt}pca_sincev~1{res}        0.043                                          -0.041                                   {txt}
{txt}{hline 108}
{txt}N           {res}          569             569             569             845             845             845   {txt}
{txt}{hline 108}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       867
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      0.04
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.9578
{txt}{col 51}R-squared{col 67}= {res}    0.0238
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0064
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0002
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.3749

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q14_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2}-.0259564{col 38}{space 2} .1248136{col 49}{space 1}   -0.21{col 58}{space 3}0.837{col 66}{space 4}-.2835591{col 79}{space 3} .2316462
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0189012{col 38}{space 2} .0783054{col 49}{space 1}   -0.24{col 58}{space 3}0.811{col 66}{space 4}-.1805157{col 79}{space 3} .1427132
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}  .043799{col 38}{space 2} .0848205{col 49}{space 1}    0.52{col 58}{space 3}0.610{col 66}{space 4}-.1312619{col 79}{space 3} .2188599
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:867}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0259564{col 36}{space 2} .1248136{col 47}{space 1}   -0.21{col 56}{space 3}0.837{col 64}{space 4}-.2835591{col 77}{space 3} .2316462
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q14_normse:mfd_q14_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:867}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0189012{col 38}{space 2} .0783054{col 49}{space 1}   -0.24{col 58}{space 3}0.811{col 66}{space 4}-.1805157{col 79}{space 3} .1427132
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 156}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)   
{txt}             fd_pca_pri~1                                     fd_q13_norm                                     fd_q14_norm                                   
{txt}{hline 156}
{txt}fd_pca_vio~1{res}        0.040                                           0.003                                          -0.008                                   {txt}
{txt}pca_sincev~1{res}        0.043                                          -0.041                                          -0.011                                   {txt}
{txt}{hline 156}
{txt}N           {res}          569             569             569             845             845             845             867             867             867   {txt}
{txt}{hline 156}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       890
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      1.05
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.3649
{txt}{col 51}R-squared{col 67}= {res}    0.0244
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0050
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0020
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2306

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q15_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2}-.0694019{col 38}{space 2} .0744731{col 49}{space 1}   -0.93{col 58}{space 3}0.361{col 66}{space 4}-.2231068{col 79}{space 3}  .084303
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0333138{col 38}{space 2} .0291247{col 49}{space 1}   -1.14{col 58}{space 3}0.264{col 66}{space 4}-.0934242{col 79}{space 3} .0267966
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} .0582447{col 38}{space 2}  .043315{col 49}{space 1}    1.34{col 58}{space 3}0.191{col 66}{space 4}-.0311531{col 79}{space 3} .1476424
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:890}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0694019{col 36}{space 2} .0744731{col 47}{space 1}   -0.93{col 56}{space 3}0.361{col 64}{space 4}-.2231068{col 77}{space 3}  .084303
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q15_normse:mfd_q15_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:890}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0333138{col 38}{space 2} .0291247{col 49}{space 1}   -1.14{col 58}{space 3}0.264{col 66}{space 4}-.0934242{col 79}{space 3} .0267966
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 204}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)            (10)            (11)            (12)   
{txt}             fd_pca_pri~1                                     fd_q13_norm                                     fd_q14_norm                                     fd_q15_norm                                   
{txt}{hline 204}
{txt}fd_pca_vio~1{res}        0.040                                           0.003                                          -0.008                                          -0.033                                   {txt}
{txt}pca_sincev~1{res}        0.043                                          -0.041                                          -0.011                                          -0.032                                   {txt}
{txt}{hline 204}
{txt}N           {res}          569             569             569             845             845             845             867             867             867             890             890             890   {txt}
{txt}{hline 204}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       850
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      1.22
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.3133
{txt}{col 51}R-squared{col 67}= {res}    0.0188
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0122
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0028
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2387

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q16_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .0855228{col 38}{space 2} .1057086{col 49}{space 1}    0.81{col 58}{space 3}0.426{col 66}{space 4} -.132649{col 79}{space 3} .3036945
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2} .0382062{col 38}{space 2}  .037258{col 49}{space 1}    1.03{col 58}{space 3}0.315{col 66}{space 4}-.0386906{col 79}{space 3}  .115103
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0520031{col 38}{space 2} .0541477{col 49}{space 1}   -0.96{col 58}{space 3}0.346{col 66}{space 4}-.1637585{col 79}{space 3} .0597524
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:850}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0855228{col 36}{space 2} .1057086{col 47}{space 1}    0.81{col 56}{space 3}0.426{col 64}{space 4} -.132649{col 77}{space 3} .3036945
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q16_normse:mfd_q16_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:850}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2} .0382062{col 38}{space 2}  .037258{col 49}{space 1}    1.03{col 58}{space 3}0.315{col 66}{space 4}-.0386906{col 79}{space 3}  .115103
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 252}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)            (10)            (11)            (12)            (13)            (14)            (15)   
{txt}             fd_pca_pri~1                                     fd_q13_norm                                     fd_q14_norm                                     fd_q15_norm                                     fd_q16_norm                                   
{txt}{hline 252}
{txt}fd_pca_vio~1{res}        0.040                                           0.003                                          -0.008                                          -0.033                                           0.040                                   {txt}
{txt}pca_sincev~1{res}        0.043                                          -0.041                                          -0.011                                          -0.032                                           0.036                                   {txt}
{txt}{hline 252}
{txt}N           {res}          569             569             569             845             845             845             867             867             867             890             890             890             850             850             850   {txt}
{txt}{hline 252}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       821
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      0.25
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.7836
{txt}{col 51}R-squared{col 67}= {res}    0.0501
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0190
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0005
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2818

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q17_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .0562857{col 38}{space 2} .0802657{col 49}{space 1}    0.70{col 58}{space 3}0.490{col 66}{space 4}-.1093745{col 79}{space 3} .2219459
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0019433{col 38}{space 2} .0450594{col 49}{space 1}   -0.04{col 58}{space 3}0.966{col 66}{space 4}-.0949413{col 79}{space 3} .0910546
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0404059{col 38}{space 2} .0491246{col 49}{space 1}   -0.82{col 58}{space 3}0.419{col 66}{space 4} -.141794{col 79}{space 3} .0609823
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:821}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0562857{col 36}{space 2} .0802657{col 47}{space 1}    0.70{col 56}{space 3}0.490{col 64}{space 4}-.1093745{col 77}{space 3} .2219459
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q17_normse:mfd_q17_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:821}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0019433{col 38}{space 2} .0450594{col 49}{space 1}   -0.04{col 58}{space 3}0.966{col 66}{space 4}-.0949413{col 79}{space 3} .0910546
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 300}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)            (10)            (11)            (12)            (13)            (14)            (15)            (16)            (17)            (18)   
{txt}             fd_pca_pri~1                                     fd_q13_norm                                     fd_q14_norm                                     fd_q15_norm                                     fd_q16_norm                                     fd_q17_norm                                   
{txt}{hline 300}
{txt}fd_pca_vio~1{res}        0.040                                           0.003                                          -0.008                                          -0.033                                           0.040                                           0.022                                   {txt}
{txt}pca_sincev~1{res}        0.043                                          -0.041                                          -0.011                                          -0.032                                           0.036                                          -0.002                                   {txt}
{txt}{hline 300}
{txt}N           {res}          569             569             569             845             845             845             867             867             867             890             890             890             850             850             850             821             821             821   {txt}
{txt}{hline 300}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       859
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      0.91
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.4145
{txt}{col 51}R-squared{col 67}= {res}    0.0211
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0095
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0010
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.3149

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q18_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .0731076{col 38}{space 2} .0847344{col 49}{space 1}    0.86{col 58}{space 3}0.397{col 66}{space 4}-.1017757{col 79}{space 3} .2479909
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2} .0244915{col 38}{space 2}  .064551{col 49}{space 1}    0.38{col 58}{space 3}0.708{col 66}{space 4}-.1087352{col 79}{space 3} .1577181
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0814061{col 38}{space 2} .0388093{col 49}{space 1}   -2.10{col 58}{space 3}0.047{col 66}{space 4}-.1615046{col 79}{space 3}-.0013077
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:859}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0731076{col 36}{space 2} .0847344{col 47}{space 1}    0.86{col 56}{space 3}0.397{col 64}{space 4}-.1017757{col 77}{space 3} .2479909
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q18_normse:mfd_q18_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:859}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2} .0244915{col 38}{space 2}  .064551{col 49}{space 1}    0.38{col 58}{space 3}0.708{col 66}{space 4}-.1087352{col 79}{space 3} .1577181
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 348}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)            (10)            (11)            (12)            (13)            (14)            (15)            (16)            (17)            (18)            (19)            (20)            (21)   
{txt}             fd_pca_pri~1                                     fd_q13_norm                                     fd_q14_norm                                     fd_q15_norm                                     fd_q16_norm                                     fd_q17_norm                                     fd_q18_norm                                   
{txt}{hline 348}
{txt}fd_pca_vio~1{res}        0.040                                           0.003                                          -0.008                                          -0.033                                           0.040                                           0.022                                           0.026                                   {txt}
{txt}pca_sincev~1{res}        0.043                                          -0.041                                          -0.011                                          -0.032                                           0.036                                          -0.002                                           0.017                                   {txt}
{txt}{hline 348}
{txt}N           {res}          569             569             569             845             845             845             867             867             867             890             890             890             850             850             850             821             821             821             859             859             859   {txt}
{txt}{hline 348}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       861
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      1.11
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.3465
{txt}{col 51}R-squared{col 67}= {res}    0.0324
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0022
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0067
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2359

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q19_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .1600481{col 38}{space 2} .1079142{col 49}{space 1}    1.48{col 58}{space 3}0.151{col 66}{space 4}-.0626758{col 79}{space 3} .3827721
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0437934{col 38}{space 2} .0479248{col 49}{space 1}   -0.91{col 58}{space 3}0.370{col 66}{space 4}-.1427053{col 79}{space 3} .0551185
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0533018{col 38}{space 2}  .045083{col 49}{space 1}   -1.18{col 58}{space 3}0.249{col 66}{space 4}-.1463485{col 79}{space 3}  .039745
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:861}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1600481{col 36}{space 2} .1079142{col 47}{space 1}    1.48{col 56}{space 3}0.151{col 64}{space 4}-.0626758{col 77}{space 3} .3827721
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q19_normse:mfd_q19_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:861}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0437934{col 38}{space 2} .0479248{col 49}{space 1}   -0.91{col 58}{space 3}0.370{col 66}{space 4}-.1427053{col 79}{space 3} .0551185
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 396}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)            (10)            (11)            (12)            (13)            (14)            (15)            (16)            (17)            (18)            (19)            (20)            (21)            (22)            (23)            (24)   
{txt}             fd_pca_pri~1                                     fd_q13_norm                                     fd_q14_norm                                     fd_q15_norm                                     fd_q16_norm                                     fd_q17_norm                                     fd_q18_norm                                     fd_q19_norm                                   
{txt}{hline 396}
{txt}fd_pca_vio~1{res}        0.040                                           0.003                                          -0.008                                          -0.033                                           0.040                                           0.022                                           0.026                                           0.076                                   {txt}
{txt}pca_sincev~1{res}        0.043                                          -0.041                                          -0.011                                          -0.032                                           0.036                                          -0.002                                           0.017                                          -0.041                                   {txt}
{txt}{hline 396}
{txt}N           {res}          569             569             569             845             845             845             867             867             867             890             890             890             850             850             850             821             821             821             859             859             859             861             861             861   {txt}
{txt}{hline 396}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       800
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      1.11
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.3469
{txt}{col 51}R-squared{col 67}= {res}    0.0317
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0008
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0018
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2623

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q20_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .0682561{col 38}{space 2}   .05346{col 49}{space 1}    1.28{col 58}{space 3}0.214{col 66}{space 4}  -.04208{col 79}{space 3} .1785922
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0410735{col 38}{space 2} .0529217{col 49}{space 1}   -0.78{col 58}{space 3}0.445{col 66}{space 4}-.1502985{col 79}{space 3} .0681515
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0095807{col 38}{space 2} .0398151{col 49}{space 1}   -0.24{col 58}{space 3}0.812{col 66}{space 4}-.0917551{col 79}{space 3} .0725937
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:800}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0682561{col 36}{space 2}   .05346{col 47}{space 1}    1.28{col 56}{space 3}0.214{col 64}{space 4}  -.04208{col 77}{space 3} .1785922
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q20_normse:mfd_q20_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:800}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0410735{col 38}{space 2} .0529217{col 49}{space 1}   -0.78{col 58}{space 3}0.445{col 66}{space 4}-.1502985{col 79}{space 3} .0681515
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 444}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)            (10)            (11)            (12)            (13)            (14)            (15)            (16)            (17)            (18)            (19)            (20)            (21)            (22)            (23)            (24)            (25)            (26)            (27)   
{txt}             fd_pca_pri~1                                     fd_q13_norm                                     fd_q14_norm                                     fd_q15_norm                                     fd_q16_norm                                     fd_q17_norm                                     fd_q18_norm                                     fd_q19_norm                                     fd_q20_norm                                   
{txt}{hline 444}
{txt}fd_pca_vio~1{res}        0.040                                           0.003                                          -0.008                                          -0.033                                           0.040                                           0.022                                           0.026                                           0.076                                           0.030                                   {txt}
{txt}pca_sincev~1{res}        0.043                                          -0.041                                          -0.011                                          -0.032                                           0.036                                          -0.002                                           0.017                                          -0.041                                          -0.035                                   {txt}
{txt}{hline 444}
{txt}N           {res}          569             569             569             845             845             845             867             867             867             890             890             890             850             850             850             821             821             821             859             859             859             861             861             861             800             800             800   {txt}
{txt}{hline 444}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001

{com}. 
. coefplot (mfd_pca_principlesneg0_1sh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_pca_principlesneg0_1si, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red)))  (mfd_q13_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q13_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q14_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q14_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q15_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q15_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q16_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q16_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q17_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q17_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q18_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q18_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q19_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q19_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q20_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q20_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))), vertical yline(0, lcolor(red)) levels(95 90) xlabel(none) groups(mfd_pca_principlesneg0_1sh mfd_pca_principlesneg0_1si = "PCA principles of neg., FD" mfd_q13_normsh mfd_q13_normsi = "UKR shouldn't negotiate, FD" mfd_q14_normsh mfd_q14_normsi = "Morally wrong to sell out, FD" mfd_q15_normsh mfd_q15_normsi = "RUS cannot be trusted, FD"  mfd_q16_normsh mfd_q16_normsi = "RUS will exploit peace, FD" mfd_q17_normsh mfd_q17_normsi = "Peace is morally right, FD" mfd_q18_normsh mfd_q18_normsi = "UKR must make peace, FD" mfd_q19_normsh mfd_q19_normsi = "UKR must make terr. concessions, FD" mfd_q20_normsh mfd_q20_normsi = "Strategic to keep negotiating, FD", labsize(small) angle(45)) aseq swapnames plotlabels("violence, short term" "violence, long term") p3(nokey) p3(nokey) p4(nokey) p5(nokey) p6(nokey) p7(nokey) p8(nokey) p9(nokey) p10(nokey) p11(nokey) p12(nokey) p13(nokey) p14(nokey) p15(nokey) p16(nokey) p17(nokey) p18(nokey) graphregion (margin(15 5 1 1)) 
{res}{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. quietly graph export figure_a14a.jpg, replace
{txt}
{com}. 
. 
. ****** FIGURE A-14b
. 
. **REGRESSING THE COMPONENTS OF PEACE ON PCA OF VIOLENCE EXPOSURE
. 
. 
. eststo clear 
{txt}
{com}.         
. foreach v in fd_pca_peacecomp0_1 fd_q21_norm fd_q22_norm fd_q23_norm fd_q24_norm fd_q25_norm fd_q26_norm fd_q27_norm fd_q28_norm fd_q29_norm{c -(}
{txt}  2{com}.         eststo m`v'se: reghdfe `v' fd_pca_violexposure0_1 pca_sinceviolexposure0_1, a(oblast_lag) vce(cluster oblast_lag)
{txt}  3{com}.         margins, dydx(fd_pca_violexposure0_1) df(24) post
{txt}  4{com}.         eststo m`v'sh
{txt}  5{com}.         est restore m`v'se
{txt}  6{com}.         margins, dydx(pca_sinceviolexposure0_1) df(24) post
{txt}  7{com}.         eststo m`v'si
{txt}  8{com}.         esttab, beta not 
{txt}  9{com}. {c )-}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       732
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}     11.04
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0004
{txt}{col 51}R-squared{col 67}= {res}    0.0678
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0334
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0331
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.0674

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}     fd_pca_peacecomp0_1{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .1027197{col 38}{space 2} .0219324{col 49}{space 1}    4.68{col 58}{space 3}0.000{col 66}{space 4} .0574535{col 79}{space 3}  .147986
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0241143{col 38}{space 2} .0136502{col 49}{space 1}   -1.77{col 58}{space 3}0.090{col 66}{space 4}-.0522869{col 79}{space 3} .0040582
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} .5378868{col 38}{space 2} .0118361{col 49}{space 1}   45.44{col 58}{space 3}0.000{col 66}{space 4} .5134583{col 79}{space 3} .5623152
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:732}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1027197{col 36}{space 2} .0219324{col 47}{space 1}    4.68{col 56}{space 3}0.000{col 64}{space 4} .0574535{col 77}{space 3}  .147986
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_pca_peacecomp0_1se:mfd_pca_peacecomp0_1se} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:732}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0241143{col 38}{space 2} .0136502{col 49}{space 1}   -1.77{col 58}{space 3}0.090{col 66}{space 4}-.0522869{col 79}{space 3} .0040582
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 60}
{txt}                      (1)             (2)             (3)   
{txt}             fd_pca_pea~1                                   
{txt}{hline 60}
{txt}fd_pca_vio~1{res}        0.171***             ***                {txt}
{txt}pca_sincev~1{res}       -0.078                                   {txt}
{txt}{hline 60}
{txt}N           {res}          732             732             732   {txt}
{txt}{hline 60}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       879
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      1.11
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.3462
{txt}{col 51}R-squared{col 67}= {res}    0.0223
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0075
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0011
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2830

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q21_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .0211833{col 38}{space 2} .0725672{col 49}{space 1}    0.29{col 58}{space 3}0.773{col 66}{space 4}-.1285881{col 79}{space 3} .1709546
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2} .0434247{col 38}{space 2} .0292378{col 49}{space 1}    1.49{col 58}{space 3}0.151{col 66}{space 4}-.0169191{col 79}{space 3} .1037685
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0253888{col 38}{space 2} .0455646{col 49}{space 1}   -0.56{col 58}{space 3}0.583{col 66}{space 4}-.1194296{col 79}{space 3} .0686519
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:879}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0211833{col 36}{space 2} .0725672{col 47}{space 1}    0.29{col 56}{space 3}0.773{col 64}{space 4}-.1285881{col 77}{space 3} .1709546
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q21_normse:mfd_q21_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:879}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2} .0434247{col 38}{space 2} .0292378{col 49}{space 1}    1.49{col 58}{space 3}0.151{col 66}{space 4}-.0169191{col 79}{space 3} .1037685
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 108}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)   
{txt}             fd_pca_pea~1                                     fd_q21_norm                                   
{txt}{hline 108}
{txt}fd_pca_vio~1{res}        0.171***             ***                        0.008                                   {txt}
{txt}pca_sincev~1{res}       -0.078                                           0.034                                   {txt}
{txt}{hline 108}
{txt}N           {res}          732             732             732             879             879             879   {txt}
{txt}{hline 108}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       876
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      0.73
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.4929
{txt}{col 51}R-squared{col 67}= {res}    0.0274
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0024
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0008
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2533

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q22_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2}-.0519738{col 38}{space 2} .0724984{col 49}{space 1}   -0.72{col 58}{space 3}0.480{col 66}{space 4}-.2016031{col 79}{space 3} .0976555
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0198396{col 38}{space 2} .0411201{col 49}{space 1}   -0.48{col 58}{space 3}0.634{col 66}{space 4}-.1047072{col 79}{space 3}  .065028
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} .0144873{col 38}{space 2} .0339306{col 49}{space 1}    0.43{col 58}{space 3}0.673{col 66}{space 4} -.055542{col 79}{space 3} .0845166
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:876}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2}-.0519738{col 36}{space 2} .0724984{col 47}{space 1}   -0.72{col 56}{space 3}0.480{col 64}{space 4}-.2016031{col 77}{space 3} .0976555
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q22_normse:mfd_q22_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:876}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0198396{col 38}{space 2} .0411201{col 49}{space 1}   -0.48{col 58}{space 3}0.634{col 66}{space 4}-.1047072{col 79}{space 3}  .065028
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 156}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)   
{txt}             fd_pca_pea~1                                     fd_q21_norm                                     fd_q22_norm                                   
{txt}{hline 156}
{txt}fd_pca_vio~1{res}        0.171***             ***                        0.008                                          -0.023                                   {txt}
{txt}pca_sincev~1{res}       -0.078                                           0.034                                          -0.017                                   {txt}
{txt}{hline 156}
{txt}N           {res}          732             732             732             879             879             879             876             876             876   {txt}
{txt}{hline 156}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       919
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      0.18
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.8330
{txt}{col 51}R-squared{col 67}= {res}    0.0254
{txt}{col 51}Adj R-squared{col 67}= {res}   -0.0030
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0003
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2373

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q23_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .0305072{col 38}{space 2} .0595279{col 49}{space 1}    0.51{col 58}{space 3}0.613{col 66}{space 4}-.0923524{col 79}{space 3} .1533667
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0088885{col 38}{space 2} .0439998{col 49}{space 1}   -0.20{col 58}{space 3}0.842{col 66}{space 4}-.0996997{col 79}{space 3} .0819226
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} .0048198{col 38}{space 2} .0434823{col 49}{space 1}    0.11{col 58}{space 3}0.913{col 66}{space 4}-.0849232{col 79}{space 3} .0945629
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:919}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0305072{col 36}{space 2} .0595279{col 47}{space 1}    0.51{col 56}{space 3}0.613{col 64}{space 4}-.0923524{col 77}{space 3} .1533667
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q23_normse:mfd_q23_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:919}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0088885{col 38}{space 2} .0439998{col 49}{space 1}   -0.20{col 58}{space 3}0.842{col 66}{space 4}-.0996997{col 79}{space 3} .0819226
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 204}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)            (10)            (11)            (12)   
{txt}             fd_pca_pea~1                                     fd_q21_norm                                     fd_q22_norm                                     fd_q23_norm                                   
{txt}{hline 204}
{txt}fd_pca_vio~1{res}        0.171***             ***                        0.008                                          -0.023                                           0.014                                   {txt}
{txt}pca_sincev~1{res}       -0.078                                           0.034                                          -0.017                                          -0.008                                   {txt}
{txt}{hline 204}
{txt}N           {res}          732             732             732             879             879             879             876             876             876             919             919             919   {txt}
{txt}{hline 204}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       897
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}     11.81
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0003
{txt}{col 51}R-squared{col 67}= {res}    0.0415
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0128
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0147
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1752

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q24_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .1149403{col 38}{space 2} .0619617{col 49}{space 1}    1.86{col 58}{space 3}0.076{col 66}{space 4}-.0129424{col 79}{space 3} .2428231
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0897731{col 38}{space 2} .0221047{col 49}{space 1}   -4.06{col 58}{space 3}0.000{col 66}{space 4}-.1353949{col 79}{space 3}-.0441513
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0009773{col 38}{space 2} .0372815{col 49}{space 1}   -0.03{col 58}{space 3}0.979{col 66}{space 4}-.0779225{col 79}{space 3} .0759679
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:897}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1149403{col 36}{space 2} .0619617{col 47}{space 1}    1.86{col 56}{space 3}0.076{col 64}{space 4}-.0129424{col 77}{space 3} .2428231
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q24_normse:mfd_q24_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:897}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0897731{col 38}{space 2} .0221047{col 49}{space 1}   -4.06{col 58}{space 3}0.000{col 66}{space 4}-.1353949{col 79}{space 3}-.0441513
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 252}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)            (10)            (11)            (12)            (13)            (14)            (15)   
{txt}             fd_pca_pea~1                                     fd_q21_norm                                     fd_q22_norm                                     fd_q23_norm                                     fd_q24_norm                                   
{txt}{hline 252}
{txt}fd_pca_vio~1{res}        0.171***             ***                        0.008                                          -0.023                                           0.014                                           0.073                                   {txt}
{txt}pca_sincev~1{res}       -0.078                                           0.034                                          -0.017                                          -0.008                                          -0.113***                             ***{txt}
{txt}{hline 252}
{txt}N           {res}          732             732             732             879             879             879             876             876             876             919             919             919             897             897             897   {txt}
{txt}{hline 252}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       889
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      2.94
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0723
{txt}{col 51}R-squared{col 67}= {res}    0.0428
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0140
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0120
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2023

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q25_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .1752267{col 38}{space 2} .0903277{col 49}{space 1}    1.94{col 58}{space 3}0.064{col 66}{space 4}-.0112004{col 79}{space 3} .3616539
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0577189{col 38}{space 2}  .035877{col 49}{space 1}   -1.61{col 58}{space 3}0.121{col 66}{space 4}-.1317654{col 79}{space 3} .0163276
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0575815{col 38}{space 2} .0506397{col 49}{space 1}   -1.14{col 58}{space 3}0.267{col 66}{space 4}-.1620968{col 79}{space 3} .0469338
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:889}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1752267{col 36}{space 2} .0903277{col 47}{space 1}    1.94{col 56}{space 3}0.064{col 64}{space 4}-.0112004{col 77}{space 3} .3616539
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q25_normse:mfd_q25_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:889}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0577189{col 38}{space 2}  .035877{col 49}{space 1}   -1.61{col 58}{space 3}0.121{col 66}{space 4}-.1317654{col 79}{space 3} .0163276
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 300}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)            (10)            (11)            (12)            (13)            (14)            (15)            (16)            (17)            (18)   
{txt}             fd_pca_pea~1                                     fd_q21_norm                                     fd_q22_norm                                     fd_q23_norm                                     fd_q24_norm                                     fd_q25_norm                                   
{txt}{hline 300}
{txt}fd_pca_vio~1{res}        0.171***             ***                        0.008                                          -0.023                                           0.014                                           0.073                                           0.096                                   {txt}
{txt}pca_sincev~1{res}       -0.078                                           0.034                                          -0.017                                          -0.008                                          -0.113***                             ***       -0.063                                   {txt}
{txt}{hline 300}
{txt}N           {res}          732             732             732             879             879             879             876             876             876             919             919             919             897             897             897             889             889             889   {txt}
{txt}{hline 300}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       894
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      4.14
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0285
{txt}{col 51}R-squared{col 67}= {res}    0.0394
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0106
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0108
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.1982

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q26_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .1604065{col 38}{space 2} .0703204{col 49}{space 1}    2.28{col 58}{space 3}0.032{col 66}{space 4} .0152723{col 79}{space 3} .3055406
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0573561{col 38}{space 2}  .038717{col 49}{space 1}   -1.48{col 58}{space 3}0.152{col 66}{space 4}-.1372641{col 79}{space 3} .0225519
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0456785{col 38}{space 2} .0449858{col 49}{space 1}   -1.02{col 58}{space 3}0.320{col 66}{space 4}-.1385246{col 79}{space 3} .0471677
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:894}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1604065{col 36}{space 2} .0703204{col 47}{space 1}    2.28{col 56}{space 3}0.032{col 64}{space 4} .0152723{col 77}{space 3} .3055406
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q26_normse:mfd_q26_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:894}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0573561{col 38}{space 2}  .038717{col 49}{space 1}   -1.48{col 58}{space 3}0.152{col 66}{space 4}-.1372641{col 79}{space 3} .0225519
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 348}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)            (10)            (11)            (12)            (13)            (14)            (15)            (16)            (17)            (18)            (19)            (20)            (21)   
{txt}             fd_pca_pea~1                                     fd_q21_norm                                     fd_q22_norm                                     fd_q23_norm                                     fd_q24_norm                                     fd_q25_norm                                     fd_q26_norm                                   
{txt}{hline 348}
{txt}fd_pca_vio~1{res}        0.171***             ***                        0.008                                          -0.023                                           0.014                                           0.073                                           0.096                                           0.090*               *                  {txt}
{txt}pca_sincev~1{res}       -0.078                                           0.034                                          -0.017                                          -0.008                                          -0.113***                             ***       -0.063                                          -0.064                                   {txt}
{txt}{hline 348}
{txt}N           {res}          732             732             732             879             879             879             876             876             876             919             919             919             897             897             897             889             889             889             894             894             894   {txt}
{txt}{hline 348}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       882
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      4.19
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0276
{txt}{col 51}R-squared{col 67}= {res}    0.0366
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0073
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0091
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2717

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q27_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .1378219{col 38}{space 2} .0701937{col 49}{space 1}    1.96{col 58}{space 3}0.061{col 66}{space 4}-.0070508{col 79}{space 3} .2826945
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.1101555{col 38}{space 2} .0453852{col 49}{space 1}   -2.43{col 58}{space 3}0.023{col 66}{space 4}-.2038259{col 79}{space 3}-.0164851
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} -.011997{col 38}{space 2} .0410473{col 49}{space 1}   -0.29{col 58}{space 3}0.773{col 66}{space 4}-.0967145{col 79}{space 3} .0727204
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:882}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1378219{col 36}{space 2} .0701937{col 47}{space 1}    1.96{col 56}{space 3}0.061{col 64}{space 4}-.0070508{col 77}{space 3} .2826945
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q27_normse:mfd_q27_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:882}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.1101555{col 38}{space 2} .0453852{col 49}{space 1}   -2.43{col 58}{space 3}0.023{col 66}{space 4}-.2038259{col 79}{space 3}-.0164851
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 396}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)            (10)            (11)            (12)            (13)            (14)            (15)            (16)            (17)            (18)            (19)            (20)            (21)            (22)            (23)            (24)   
{txt}             fd_pca_pea~1                                     fd_q21_norm                                     fd_q22_norm                                     fd_q23_norm                                     fd_q24_norm                                     fd_q25_norm                                     fd_q26_norm                                     fd_q27_norm                                   
{txt}{hline 396}
{txt}fd_pca_vio~1{res}        0.171***             ***                        0.008                                          -0.023                                           0.014                                           0.073                                           0.096                                           0.090*               *                          0.057                                   {txt}
{txt}pca_sincev~1{res}       -0.078                                           0.034                                          -0.017                                          -0.008                                          -0.113***                             ***       -0.063                                          -0.064                                          -0.089*                               *  {txt}
{txt}{hline 396}
{txt}N           {res}          732             732             732             879             879             879             876             876             876             919             919             919             897             897             897             889             889             889             894             894             894             882             882             882   {txt}
{txt}{hline 396}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       896
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      3.53
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0452
{txt}{col 51}R-squared{col 67}= {res}    0.0292
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0002
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0070
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2177

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q28_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .1538975{col 38}{space 2} .0584166{col 49}{space 1}    2.63{col 58}{space 3}0.015{col 66}{space 4} .0333315{col 79}{space 3} .2744634
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2} .0249482{col 38}{space 2} .0394393{col 49}{space 1}    0.63{col 58}{space 3}0.533{col 66}{space 4}-.0564505{col 79}{space 3}  .106347
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0703863{col 38}{space 2} .0393377{col 49}{space 1}   -1.79{col 58}{space 3}0.086{col 66}{space 4}-.1515753{col 79}{space 3} .0108027
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:896}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .1538975{col 36}{space 2} .0584166{col 47}{space 1}    2.63{col 56}{space 3}0.015{col 64}{space 4} .0333315{col 77}{space 3} .2744634
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q28_normse:mfd_q28_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:896}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2} .0249482{col 38}{space 2} .0394393{col 49}{space 1}    0.63{col 58}{space 3}0.533{col 66}{space 4}-.0564505{col 79}{space 3}  .106347
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 444}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)            (10)            (11)            (12)            (13)            (14)            (15)            (16)            (17)            (18)            (19)            (20)            (21)            (22)            (23)            (24)            (25)            (26)            (27)   
{txt}             fd_pca_pea~1                                     fd_q21_norm                                     fd_q22_norm                                     fd_q23_norm                                     fd_q24_norm                                     fd_q25_norm                                     fd_q26_norm                                     fd_q27_norm                                     fd_q28_norm                                   
{txt}{hline 444}
{txt}fd_pca_vio~1{res}        0.171***             ***                        0.008                                          -0.023                                           0.014                                           0.073                                           0.096                                           0.090*               *                          0.057                                           0.079*               *                  {txt}
{txt}pca_sincev~1{res}       -0.078                                           0.034                                          -0.017                                          -0.008                                          -0.113***                             ***       -0.063                                          -0.064                                          -0.089*                               *          0.026                                   {txt}
{txt}{hline 444}
{txt}N           {res}          732             732             732             879             879             879             876             876             876             919             919             919             897             897             897             889             889             889             894             894             894             882             882             882             896             896             896   {txt}
{txt}{hline 444}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 1 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}       885
{txt}Absorbing 1 HDFE group{col 51}F({res}   2{txt},{res}     24{txt}){col 67}= {res}      4.11
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0293
{txt}{col 51}R-squared{col 67}= {res}    0.0304
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0010
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0054
{txt}{col 1}Number of clusters ({res}oblast_lag{txt}) {col 30}= {res}        25{txt}{col 51}Root MSE{col 67}= {res}    0.2065

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:oblast_lag})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             fd_q29_norm{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fd_pca_violexposure0_1 {c |}{col 26}{res}{space 2} .0646366{col 38}{space 2} .0565044{col 49}{space 1}    1.14{col 58}{space 3}0.264{col 66}{space 4}-.0519828{col 79}{space 3}  .181256
{txt}pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0687962{col 38}{space 2} .0244202{col 49}{space 1}   -2.82{col 58}{space 3}0.010{col 66}{space 4} -.119197{col 79}{space 3}-.0183954
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.0050599{col 38}{space 2} .0303081{col 49}{space 1}   -0.17{col 58}{space 3}0.869{col 66}{space 4}-.0676126{col 79}{space 3} .0574929
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}  oblast_lag{col 14}{c |}{space 1}       25{col 27}{space 1}       25{col 39}{result}{space 1}        0{col 53}{text}*{col 54}{c |}
{res}{col 1}{text}{hline 13}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:885}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:fd_pca_violexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   std. err.{col 48}      t{col 56}   P>|t|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fd_pca_violexposure0_1 {c |}{col 24}{res}{space 2} .0646366{col 36}{space 2} .0565044{col 47}{space 1}    1.14{col 56}{space 3}0.264{col 64}{space 4}-.0519828{col 77}{space 3}  .181256
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(results {stata estimates replay mfd_q29_normse:mfd_q29_normse} are active now)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:885}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:pca_sinceviolexposure0_1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}      dy/dx{col 38}   std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pca_sinceviolexposure0_1 {c |}{col 26}{res}{space 2}-.0687962{col 38}{space 2} .0244202{col 49}{space 1}   -2.82{col 58}{space 3}0.010{col 66}{space 4} -.119197{col 79}{space 3}-.0183954
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 492}
{txt}                      (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)             (9)            (10)            (11)            (12)            (13)            (14)            (15)            (16)            (17)            (18)            (19)            (20)            (21)            (22)            (23)            (24)            (25)            (26)            (27)            (28)            (29)            (30)   
{txt}             fd_pca_pea~1                                     fd_q21_norm                                     fd_q22_norm                                     fd_q23_norm                                     fd_q24_norm                                     fd_q25_norm                                     fd_q26_norm                                     fd_q27_norm                                     fd_q28_norm                                     fd_q29_norm                                   
{txt}{hline 492}
{txt}fd_pca_vio~1{res}        0.171***             ***                        0.008                                          -0.023                                           0.014                                           0.073                                           0.096                                           0.090*               *                          0.057                                           0.079*               *                          0.035                                   {txt}
{txt}pca_sincev~1{res}       -0.078                                           0.034                                          -0.017                                          -0.008                                          -0.113***                             ***       -0.063                                          -0.064                                          -0.089*                               *          0.026                                          -0.074**                              ** {txt}
{txt}{hline 492}
{txt}N           {res}          732             732             732             879             879             879             876             876             876             919             919             919             897             897             897             889             889             889             894             894             894             882             882             882             896             896             896             885             885             885   {txt}
{txt}{hline 492}
{txt}Standardized beta coefficients
{txt}* p<0.05, ** p<0.01, *** p<0.001

{com}.         
. 
. coefplot (mfd_pca_peacecomp0_1sh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_pca_peacecomp0_1si, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red)))  (mfd_q21_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q21_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q22_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q22_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q23_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q23_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q24_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q24_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q25_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q25_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q26_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q26_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q27_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q27_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q28_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q28_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))) (mfd_q29_normsh, msymbol(d) msize(normal) mcolor(blue) ciopts(lcolor(blue blue))) (mfd_q29_normsi, msymbol(o) msize(normal) mcolor(red) ciopts(lcolor(red red))), vertical yline(0, lcolor(red)) levels(95 90) xlabel(none) groups(mfd_pca_peacecomp0_1sh mfd_pca_peacecomp0_1si = "PCA peace components, FD" mfd_q21_normsh mfd_q21_normsi = "UKR shouldn't join NATO, FD" mfd_q22_normsh mfd_q22_normsi = "Western security guarantees, FD" mfd_q23_normsh mfd_q23_normsi = "Russian as official language, FD"  mfd_q24_normsh mfd_q24_normsi = "Crimea as part of Russia, FD" mfd_q25_normsh mfd_q25_normsi = "Independent DNR/LNR, FD" mfd_q26_normsh mfd_q26_normsi = "Reduce UKR army size, FD" mfd_q27_normsh mfd_q27_normsi = "Voting by DNR/LNR, FD" mfd_q28_normsh mfd_q28_normsi = "UKR reject joining EU, FD" mfd_q29_normsh mfd_q29_normsi = "Zelensky stepping down, FD", labsize(small) angle(45)) aseq swapnames plotlabels("violence, short term" "violence, long term") p3(nokey) p4(nokey) p5(nokey) p6(nokey) p7(nokey) p8(nokey) p9(nokey) p10(nokey) p11(nokey) p12(nokey) p13(nokey) p14(nokey) p15(nokey) p16(nokey) p17(nokey) p18(nokey) p19(nokey) p20(nokey) graphregion (margin(15 5 1 1)) 
{res}{p 0 4 2}
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{p 0 4 2}
{txt}(note:  named style
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symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
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symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
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symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
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symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
normal not found in class
symbolsize,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. 
. quietly graph export figure_a14b.jpg, replace
{txt}
{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/Tolga/Dropbox/Ukraine_SurveyExp/Repository/code/REPLICATION/Replication_PSRM/RedLines_Replication.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}20 Nov 2025, 16:28:29
{txt}{.-}
{smcl}
{txt}{sf}{ul off}